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The purpose of this paper is to identify the leading topics and developmental trends of archival information resource research in China by visualizing the intellectual structure and evolution patterns of archival information resource research.

This study took China National Knowledge Infrastructure (CNKI) as the data source and extracted keywords from relevant articles in archival information resource research as the sample. First, the frequency and co-occurrence of keywords were calculated by using SCI2. Second, this study analyzed the co-word network indicators by using Pajek. Then, topic community detection was conducted by using a VOS viewer, as well as the visualization of intellectual structures. Next, this study developed a graphical mapping of the evolution of research topics over time by using Cortext.

The research topics of archival information resources in China were unbalanced but distinct. Researchers focus on the construction and utilization of archival information resource, which consist of five evident research directions. The phenomena of fusion and differentiation coexist in research topic evolution. There were both continuities of traditional research and innovations in emerging research. The archival information resource research tended to be systematized and extended, reflecting the vertical and horizontal extension of the research content.

Based on a large number of previous studies, this study adopted quantitative methods to reveal the intellectual structure and evolution patterns of archival information resource research in China, providing guidance for researchers and institutions to grasp research status and developmental trends.

The purpose of this paper is to measure the degree of interdisciplinary collaboration in Big Data research based on the co-occurrences of subject categories using Stirling’s diversity index and specialization index.

Interdisciplinarity was measured utilizing the descriptive statistics of disciplines, network indicators showing relationships between disciplines and within individual disciplines, interdisciplinary communities, Stirling’s diversity index and specialization index, and a strategic diagram revealing the development status and trends of discipline communities.

Comprehensively considering all results, the degree of interdisciplinarity of Big Data research is increasing over time, particularly, after 2013. There is a high level of interdisciplinarity in Big Data research involving a large number of disciplines, but it is unbalanced in distribution. The interdisciplinary collaborations are not intensive on the whole; most disciplines are aggregated into a few distinct communities with computer science, business and economics, mathematics, and biotechnology and applied microbiology as the core. Four major discipline communities in Big Data research represent different directions with different development statuses and trends. Community 1, with computer science as the core, is the most mature and central to the whole interdisciplinary network. Accounting for all network indicators, computer science, engineering, business and economics, social sciences, and mathematics are the most important disciplines in Big Data research.

This study deepens our understanding of the degree and trend of interdisciplinary collaboration in Big Data research through a longitudinal study and quantitative measures based on two indexes. It has practical implications to study and reveal the interdisciplinary phenomenon and characteristics of related developments of a specific research area, or to conduct comparative studies between different research areas.

The purpose of this paper is to visualize the collaboration network among regions in library science (LS) in China. Using various methods and tools of social network analysis and geographical visualization, results were obtained, showing the structure and patterns of research collaborations in topological and geographical views, as well as the geographical distributions of contribution.

The sample includes all studies published in the top journal in library science in China from 2006 to 2015. First, co-occurrence data representing collaborations among regions was extracted from author affiliations. Second, the topological network of collaboration was generated by applying social network analysis tools and descriptive statistics, network indicators of the collaboration network and research communities were calculated. Third, the topological network was projected into a geographical map with corresponding coordinates and distances using geographical tools. Finally, the topological network maps and the geographical maps were produced for visualization.

The levels of contribution are very unbalanced between regions, and overall research collaboration is low. Beijing, Hubei and several other areas are central and significant regions in China; other regions are mostly connected with central ones through direct collaborations. Research collaborations in LS research in China are mostly distributed in the east and south of China, being centralized in the “Beijing–Hubei–Shanghai” triangle zone, as well as within the triangle’s extending zones. Finally, there are three distinct research communities that connect closely within themselves and loosely between them. The Beijing community is relatively centralized in geography, while other communities are scattered.

This study applied various methods and tools of social network analysis and geographical mapping analysis to reveal the collaboration structure and patterns among regions in LS research in China. Visualized maps in topological and geographical views help shed new light on research efforts.

The purpose of this paper is to analyze the research status and outputs of information behavior in China in order to reveal its in-depth research pattern and trends.

The author collected literature during the past 29 years from China Academic Journal Network Publishing Database. Bibliometric analysis, including publication growth analysis, core authors and collaborative degree analysis, core journals analysis, and institutions distribution, was performed. The temporal visualization map of burst term was drawn, and the co-occurrence matrix of these keywords was analyzed by the hierarchical cluster analysis, strategic diagram, and social network analysis.

The earliest article on information behavior in China was published in 1987. And the number of articles has risen continually since then, which follows the logical growth law of literature. The collaborative degree of authors is on the rise in general. The distribution of these articles obeys the Bradford’s Law of Scattering. School of Information Management of Wuhan University remarkably ranks the top in most publications. In all, ten important research directions were identified, which are in the imbalanced development. And a newly appearing topic with great potential for further development, namely information seeking and information security, is identified.

This study provides important insights into the research status and trends on information behavior in China, which might provide a potential guide for the future research.

The purpose of this paper is to examine behavior intention of Chinese university students toward mobile library (m-library) applications (apps) and to explore the determinants of their perceptions of m-library apps.

Based on previous research, this study first proposed an integrated model and hypothesized the relationships between constructs while exploring the significance of the effects of education level and discipline. In all, 466 undergraduate and graduate students from various disciplines participated in a survey for this study. A structural equation modeling approach was applied for analysis.

Attitude is the main predictor of behavior intention toward m-library apps, and it is significantly affected by perceived usefulness, self-efficacy, and subjective norm. The quality of m-library apps determines students’ perception of usefulness. In addition, education level and discipline have significant effects on behavior intention toward m-library apps, which may reflect students’ different focuses and purposes of use.

This study deepens understanding of the continuance usage of m-library apps and has practical implications on related developments and services.

This study proposes a novel method utilising a speech-word pair bipartite network to examine the correlation structure between members of parliament (MPs) in the context of the UK- China relationship.

We construct MP-word pair bipartite networks based on the co-occurrence relationship between MPs and words in their speech content. These networks are then mapped into monopartite MPs correlation networks. Additionally, the study calculates correlation network indicators and identifies MP communities and factions to determine the characteristics of MPs and their interrelation in the UK-China relationship. This includes insights into the distribution of key MPs, their correlation structure and the evolution and development trends of MP factions.

Analysis of the parliamentary speeches on China-related affairs in the British Parliament from 2011 to 2020 reveals that the distribution and interrelationship of MPs engaged in UK-China affairs are centralised and discrete, with a few core MPs playing an integral role in the UK-China relationship. Among them, MPs such as Lord Ahmad of Wimbledon, David Cameron, Lord Hunt of Chesterton and Lord Howell of Guildford formed factions with significant differences; however, the continuity of their evolution exhibits unstableness. The core MP factions, such as those led by Lord Ahmad of Wimbledon and David Cameron, have achieved a level of maturity and exert significant influence.

The research has several limitations that warrant acknowledgement. First, we mapped the MP-word pair bipartite network into the MP correlation network for analysis without directly analysing the structure of MPs based on the bipartite network. In future studies, we aim to explore various types of analysis based on the proposed bipartite networks to provide more comprehensive and accurate references for studying UK-China relations. In addition, we seek to incorporate semantic-level analyses, such as sentiment analysis of MPs, into the MP-word -pair bipartite networks for in-depth analysis. Second, the interpretations of MP structures in the UK-China relationship in this study are limited. Consequently, expertise in UK-China relations should be incorporated to enhance the study and provide more practical recommendations.

Firstly, the findings can contribute to an objective understanding of the characteristics and connotations of UK-China relations, thereby informing adjustments of focus accordingly. The identification of the main factions in the UK-China relationship emphasises the imperative for governments to pay greater attention to these MPs’ speeches and social relationships. Secondly, examining the evolution and development of MP factions aids in identifying a country’s diplomatic focus during different periods. This can assist governments in responding promptly to relevant issues and contribute to the formulation of effective foreign policies.

First, this study expands the research methodology of parliamentary debates analysis in previous studies. To the best of our knowledge, we are the first to study the UK-China relationship through the MP-word-pair bipartite network. This outcome inspires future researchers to apply various knowledge networks in the LIS field to elucidate deeper characteristics and connotations of UK-China relations. Second, this study provides a novel perspective for UK-China relationship analysis, which deepens the research object from keywords to MPs. This finding may offer important implications for researchers to further study the role of MPs in the UK-China relationship.

This study proposes a novel scheme for analysing the correlation structure between MPs based on bipartite networks. This approach offers insights into the development and evolving dynamics of MPs.

The purpose of this paper is to reveal the structure and patterns of cross-national collaborations in Big Data research through application of various social network analysis and geographical visualization methods.

The sample includes articles containing Big Data research, covering all years, in the Web of Science Core Collection as of December 2015. First, co-occurrence data representing collaborations among nations were extracted from author affiliations. Second, the descriptive statistics, network indicators of collaborations, and research communities were calculated. Third, topological network maps, geographical maps integrated with topological network projections, and proportional maps were produced for visualization.

The results show that the scope of international collaborations in Big Data research is broad, but the distribution among nations is unbalanced and fragmented. The USA, China, and the UK were identified as the major contributors to this research area. Five research communities are identified, led by the USA, China, Italy, South Korea, and Brazil. Collaborations within each community vary, reflecting different levels of research development. The visualizations show that nations advance in Big Data research are centralized in North America, Europe, and Asia-Pacific.

This study applied various informetric methods and tools to reveal the collaboration structure and patterns among nations in Big Data research. Visualized maps help shed new light on global research efforts.

The purpose of this paper is to invent a new functional coated fabric based on nanomaterials to shield UV and IR. Multifunctional surface coatings with ultraviolet (UV)/near infrared radiations protection and waterproof were widely applied in outdoor fabrics. Herein, ultrafine TiO2 and nano-antimony doped tin dioxide (ATO) were prepared and embedded into water-based polyurethane (PU) coatings and then coated on the nylon fabric.

ATO was prepared using the sol–gel method and the two powders were dispersed by ball milling. The results of zeta potential and particle size distribution showed that the ultrafine TiO2 and nano-ATO could be stably dispersed in water at pH 8 with the presence of sodium polycarboxylate. The optimal process was screened out by orthogonal design and scanning electron microscopy (SEM), UV protection, thermal insulation and water-pressure resistance were tested. SEM images indicated the nanoparticles could be uniformly dispersed in the coatings.

The effect of UV prevention can get to UPF > 50, UVA < 5 per cent, which meet up with the AATCC 183-2014. Coatings can effectively lower the temperature of fabric surface by 8∼9ºC through the self-made closed test system and by 3ºC through the open test system.

These PU coatings are environment-friendly and adhesive to impart waterproof, UV-proof and thermal insulation properties to nylon fabrics by coating finishing.

This study investigates the impact of network positions on inter-team conflicts and project success in megaprojects.

Network position is measured with centrality and structural holes. Substantive conflict and affective conflict reflect inter-team conflicts. A questionnaire survey was implemented in Chinese megaprojects, and 309 valid questionnaires were collected. The data were analyzed using structural equation modeling and bootstrapping methods.

The results show that centrality negatively impacts project success, while the presence of a team in a structural hole has no significant impact on project success. Centrality is negatively related to substantive conflict and is positively related to affective conflict. The team in a structural hole has a positive effect on substantive conflict and a negative effect on affective conflict. Substantive conflict and affective conflict have positive and negative effects on project success, respectively. The effect of network position on project success is mediated by inter-team conflict.

This research provides a reference for megaproject managers to better conduct network governance, manage inter-team conflict, and successfully manage projects. The study did not investigate the effects of changes in teams' network positions on project success. Future research should explore this facet of megaprojects.

This research adds to existing research on network position, and reveals that project network governance is important for megaproject success. This provides a new direction for megaproject management. Furthermore, the results validate constructive and non-constructive roles and the mediating role of inter-team conflict. This complements the literature on conflict management, providing a reference for megaproject managers when managing inter-team conflict.

In prior literature, online endorsement system allowing the users to “like” or “dislike” shared information is found very useful in information filtering and trust elicitation in most social networks. This paper shows that such systems could fail in the context of investment communities due to several psychological biases.

This study develops a series of regression analyses to model the “like”/“dislike” voting process and whether or not such endorsement distinguishes between valuable information and noise. Trading simulations are also used to validate the practical implications of the findings.

The main findings of this research are twofold: (1) in the context of investment communities, online endorsement system fails to signify value-relevant information and (2) bullish information and “wisdom over the past event” information receive more “likes” and fewer “dislikes” on average, but they underperform in stock market price discovery.

This study demonstrates that biased endorsement may lead to the failure of the online endorsement system as information gatekeeper in investment communities. Two underlying mechanisms are proposed and tested. This study opens up new research opportunities to investigate the causes of biased endorsement in online environment and motivates the development of alternative information filtering systems.