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The purpose of this study is threefold. The first is to conduct a comprehensive examination of the various board attributes to corporate social responsibility (CSR) reporting in the Chinese technology industry. The second is to investigate the impact of ownership and board attributes on CSR. The third is to examine the moderating effect of media reporting on the relationship between CSR and company financial performance.

All A-share listed Chinese companies during the years 2011–2019 with 1,573 firm-year observations have been investigated for this study. The data are analysed by CSR metrics in the form of environmental, social and governance (ESG) scores using an ordinary least squares regression analysis and fixed effect regression models.

The results of this longitudinal study reveal that; no matter whether the companies are state-own or non-state-own, there is a significant positive effect of board independence, monetary incentives, director’s age and board size on the CSR disclosure of the Chinese technology industry. Also, the results support the importance of CSR performance in promoting the corporate financial performance (CFP) of the technology sector. Specifically, media reporting has a positive impact on the CSR reporting of both state-own and non-state-own technological companies in China.

To the best of the authors’ knowledge, this is the first study based on the ESG metrics for analysing the CSR and firm performance relationship conducted in the unique setting of the state-own and non-state-own technological companies in China. The study is an attempt to fill the gap in the extant literature, which has a scarce number of studies focused on the influence of media reporting on the relationship between CSR performance and CFP. This paper not only updates the existing understanding of CSR performance by board attributes and company ownership but also explains the significance of media reporting in enhancing the CSR performance of the Chinese technology industry.

This article aims to understand users' information needs in the metaverse communities and to analyze the similarities and differences between their information needs and those of users in Internet communities.

This study conducted semi-structured interviews with users in the metaverse communities to gather raw data. Grounded theory research methods were employed to code and analyze the collected interview data, resulting in the extraction of 40 initial concepts, 15 subcategories and 5 main categories. Based on Maslow’s hierarchy of needs theory, this paper constructs the hierarchical model of users' information needs in the metaverse communities. It compares the differences between users' information needs in the metaverse and Internet fields.

The user’s information needs in the metaverse communities are divided into two types: deficiency needs and growth needs. Deficiency needs have two levels. The first level is the demand for basic information resources. The second level is the users demand for information assistance. Growth needs have three levels. The first level is the need for information interactions. The second level is the need for community rules. The ownership information in the community rules can provide proof of user status, assets and so on. The third level is the need for users to contribute and share their own created information content.

This article presents the latest research data from in-depth interviews with users in the metaverse communities. It aims to help builders and managers of metaverse communities understand users' information needs and improve the design of virtual communities.

This paper aims to propose a conceptual model for improving the organization of user needs information in the big data environment.

A conceptual model of the organization of user needs information based on Linked Data techniques is constructed. This model has three layers: the Data Layer, the Semantic Layer and the Application Layer.

Requirements for organizing user needs information in the big data environment are identified as follows: improving the intelligence level, establishing standards and guidelines for the description of user needs information, enabling the interconnection of user needs information and considering individual privacy in the organization and analysis of user needs.

This Web of Needs model could be used to improve knowledge services by matching user needs information with increasing semantic knowledge resources more effectively and efficiently in the big data environment.

This study proposes a conceptual model, the Web of Needs model, to organize and interconnect user needs. Compared with existing methods, the Web of Needs model satisfies the requirements for the organization of user needs information in the big data environment with regard to four aspects: providing the basis and conditions for intelligent processing of user needs information, using RDF as a description norm, enabling the interconnection of user needs information and setting various protocols to protect user privacy.

In order to analyze the level of independent controllability and its evolution of high-end equipment manufacturing industry from Jiangsu Province, this article introduces the dual-excitation control line method to construct a comprehensive evaluation model for independent controllability.

Through the collection of information of high-end equipment manufacturing industry's independent and controllable capabilities on different indicators, the three aspects of advancement, autonomy and controllability, an empirical evaluation of 10 enterprises in the high-end equipment cluster in Jiangsu Province was conducted in terms of advancement, autonomy and controllability.

It effectively reveals the area and evolution characteristics of the “reward” and “punishment” of different indicators of each representative enterprise and reflects the development status and different characteristics of each representative enterprise on the three indicators. The research results provide decision-making guidance for enterprises in the management and control of advanced manufacturing systems with independent and controllable capabilities.

Existing research focuses on the evaluation of enterprises' independent controllability only on a single angle or index. This paper maps the dynamic evaluation problem of multiple time-point data to the evaluation problem of single time-point multi-index data and investigates the fluctuation of the performance of the same enterprise under different indexes, so as to comprehensively evaluate the independent controllable level of high-end equipment manufacturing industry and analyze the reasons. Further, this paper first establishes an evaluation index system of independent controllable level of high-end equipment manufacturing industry and quantitatively measures the advanced, independent, controllable and other aspects of typical enterprises in this industry by constructing a double incentive control line evaluation model.

The purpose of this paper is to achieve the multi-objective optimization design of novel tubular switched reluctance motor (TSRM).

First, the structure and initial dimensions of TSRM are obtained based on design criteria and requirements. Second, the sensitivity analysis rules, process and results of TSRM are performed. Third, three optimization objectives are determined by the average electromagnetic force, smoothing coefficient and copper loss ratio. The analytic hierarchy process-entropy method-a technique for order preference by similarity to an ideal solution-grey relation analysis comprehensive evaluation algorithm is used to optimize TSRM. Finally, a prototype is manufactured, a hardware platform is built and static and dynamic experimental validations are carried out.

The sensitivity analysis reveals that parameters significantly impact the performance of TSRM. The results of multi-objective optimization show that the average electromagnetic force and smoothing coefficient after optimization are better than before, and the copper loss ratio reduces slightly. The experimental and simulated results of TSRM are consistent, which verifies the accuracy of TSRM.

In this paper, only three optimization objectives are selected in the multi-objective optimization process. To improve the performance of TSRM, the heating characteristics, such as iron loss, can be considered as the optimization objective for a more comprehensive analysis of TSRM performance.

A novel motor structure is designed, combining the advantages of the TSRM and the linear motor. The established sensitivity analysis rules are scientific and suitable for the effects of various parameters on motor performance. The proposed multi-objective optimization algorithm is a comprehensive evaluation algorithm. It considers subjective weight and objective weight and fully uses the original data and the relational degree between the optimization objectives.

The purpose of this paper is to explore the impact of multidimensional perceived value and perceived pressure on physicians’ continuous intention to use (CIU) online health communities (OHCs) based on perceived value (PV) theory and conservation of resources (COR) theory.

This study developed a research model to test the proposed hypotheses, and the proposed model was tested using partial least squares structural equation modelling (PLS-SEM) for which data were collected from 481 physicians with OHC experience using an online survey.

The empirical results show the following: (1) Physicians’ CIU is influenced by perceived value and perceived pressure, with attitude towards OHCs using (ATU) playing a crucial role in the pathways. (2) Additional value, work pressure, peer pressure and social pressure have a positive impact on CIU, with consultation value, relationship value, work pressure and peer pressure positively influencing CIU through ATU as a mediator. (3) Reputation value has a positive effect on CIU moderated by seniority (online seniority and offline seniority).

This study emphasises the importance of different dimensions of perceived value and perceived pressure in CIU. Meanwhile, we broaden the research scope of PV theory and COR theory and provide inspiration to OHC managers and healthcare institution managers.

This study considers online searching by health information consumers as a learning process. We focus on search sequences, query reformulation, and conceptual changes.

A qualitative user study (30 participants; three health information seeking tasks) investigated mobile searching behavior. Recorded screen activity, questionnaires, and in-depth personal interview data were collected and analyzed.

(1) Search platform sequences of health information consumers in search as a learning process were exacted and their features were highlighted. (2) Query sequence and reformulation pattern of health information consumers were exacted and discussed. (3) The types and degree of conceptual changes of health consumers were reflected by their query reformulation behavior and differ from different health information search tasks. (4) Characteristics of health consumers' search as learning process were revealed.

(1) A novel perspective of consumer health information studies was proposed by exacting search platform sequence, query sequence and linking them with conceptual changes during the search as learning process. (2) Conceptual changes in the searching as a learning process are regarded as a measure of search outcome in this study, in which terms extracted from queries were used to reflect conceptual changes in consumers' mind. (3) Our findings provide evidences that types of health information seeking tasks do have significant influences on the search as a learning process.

The findings of this study can lead to the fit-to-needs of the search platforms, provide advice for information architecture of search list of search platforms, and guide the design of knowledge graph of health information systems.

Potential relationships between information-seeking behavior and conceptual changes in search as a learning process relative to health information were revealed.

From practical perspectives and to improve the working efficiency, trolley transportation and payload hoisting/lowering should be simultaneously controlled. Moreover, in practical crane applications, the transportation time is an important criterion for improving transportation efficiency. Based on these requirements, this paper aims to solve positioning and antiswing control problems and shorten the transportation time for underactuated varying-rope-length overhead cranes.

By choosing trolley acceleration and varying-rope-length acceleration as system inputs, the crane system dynamic model is converted into an equivalent model without linearizing/approximating. Then, based on the converted model and system state constraints, a time-optimal problem is formulated. Further, the original problem is converted into an optimization problem with algebraic constraints which can be conveniently solved. Finally, by solving the optimization problem, the optimal trajectories of system states, including displacements, velocities and accelerations, are obtained.

This paper first provides a nonlinear time-optimal trajectory planner for varying-rope-length overhead cranes, which achieves accurate and fast trolley positioning and eliminates payload residual swings. Meanwhile, all system states satisfy the given constraints during the entire process. Hardware experimental results show that the proposed time-optimal planner is effective and has better performance compared with existing methods.

This paper proposes a time-optimal trajectory planner for overhead crane systems with hoisting/lowering motion. The proposed planner achieves fast trolley positioning and eliminates payload residual swing with all the system states being constrained within given scopes. The planner is presented based on the original nonlinear system dynamics without linearization/approximation.

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI.

An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users (N = 244).

According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI.

This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.

This study aims to explore the influence of topic familiarity on the four stages of college students' learning search process.

This study clarified the effects of topic familiarity on students' learning search process by conducting a simulation experiment based on query formulation, information item selection, information sources and learning output.

The results characterized users' interaction behaviors in increasing topic familiarity through their use of more task descriptions as queries, increased reformulation of queries, construction of more purposeful query formulation, reduced attention to a topic's basic concept content and increased exploration of academic platform contents.

This study proposed three innovative indicators which were proposed to evaluate the effects of topic familiarity on college students' learning search process, and the adopted metrics were useful for observing differences in college students' learning output as their topic familiarity increased. It contributes to the understanding of a user's search process and learning output to support the optimization function of learning-related information search systems and improve their effect on the user's search process for learning.