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This investigation is designed to quantify and appraise the efficiency of resource distribution in the provision of public digital cultural services in China. By acknowledging and incorporating the realities of China's social development, the authors offer recommendations for enhancement derived from the study’s data analysis results. The research zeroes in on the dissection and analysis of the integral elements that structure the provision of public digital cultural services, and it concentrates on the associated data computation. The conclusions drawn herein are expected to serve as a significant point of reference for ongoing academic investigations and practical explorations in affiliated domains.
In this research, the authors utilize a hybrid methodology to meticulously evaluate the efficiency of the components that underpin the provision of public digital cultural services (PDCS) in China. The authors embark on deconstructing the various constituents within the PDCS supply framework, conducting in-depth analyses and providing cogent interpretations of each integral element. Subsequently, the authors deploy the well-regarded SBM super-efficiency model to ascertain the operational efficiency of these components. Ultimately, through a comprehensive interpretation of the measured data and the integration of extant societal development conditions, the authors put forth relevant recommendations.
The provision of PDCS in China as of 2021 had been characterized by overall good efficiency, significant regional disparity and a disconnect between inputs and outputs with weak correlations to economic and demographic data.
In this study, the authors provide an exhaustive deconstruction and interpretation of the public digital cultural services supply system, thereby proposing a framework for evaluating the efficiency of supply element allocation. Additionally, the authors have determined a set of distinct measurable indicators that are readily accessible for open collection. Notably, this analytical and evaluative framework designed for element analysis and measurement may also find application in efficiency evaluation research of the supply systems of other related cultural endeavors.
The research in this paper aims to investigate the development of Library and Information Science in Chinese universities. Specifically, it focuses on understanding the spatial and temporal aspects of subject knowledge output and providing a more comprehensive explanation of the imbalance in subject research.
This study applies the bibliometric method to analyze 131,112 papers published by 51 universities in mainland China from 1977 to 2021, as recorded in the Chinese Social Sciences Citation Index (CSSCI). The study classifies the evolution trends of the discipline and quantifies the published article data of the universities using the index of published articles. Additionally, it examines the development status, structural situation, influencing factors and prospects of universities in different categories and regions.
The field of Library and Information Science is gaining momentum in Chinese universities, but there are significant differences in its development. While the relative gap among universities in a regional context is diminishing, the absolute gap in the category perspective is increasing. The development of Library and Information Science is influenced by various factors, including the academic environment, geographical position, scientific research projects and academic traditions. The uneven development of the discipline is maintained in the short term.
This paper proposes a new quantitative index of discipline development, the university publication index. This index allows for an examination of the temporal and spatial trends of discipline development using domestic universities as the subject of research. The paper presents an overview of discipline development through four aspects: academic participation practice, discipline governance mechanisms, education and teaching systems and discourse construction within the discipline. The theoretical support provided by this study can help facilitate innovative development in the discipline.
Adopting and implementing robotic technology applications in the library is a significant technological up-gradation today. The purpose of this study was to evaluate selected literature focused mainly on robotics technology applications in the field of libraries and to assess the online social attention to research publications.
The study employed Scientometric and altmetric tools to evaluate the research publications. The bibliographic data of research publications were downloaded from Scopus database and scrutinized one by one and 71 articles were selected which mainly focused on robotic technology in libraries. Altmetric data were collected from the Dimensions.ai database. The analysis was performed using MS Excel, Tableau, Biblioshiny, VOSviewer and SPSS software.
Research on robotic technology in the field of libraries has been experiencing a gradual increase, marked by an annual growth rate of 12.93%. The United States has prominently led the way as the most active participant and collaborator in this advancement. Among the various journals, Library Hi Tech has notably stood out as a significant contributor to this field. However, the research articles have garnered limited social attention and impact. Furthermore, the patterns of authorship collaboration have demonstrated relatively modest levels within the field, and a weak correlation has been observed between the social attention received and the Scopus citation metrics of the publications.
The research needs to be disseminated more through various social media platforms to increase its visibility. Sharing research information through social media can bridge the gap between academia and society. The findings of this study can serve as a valuable reference for researchers and policymakers.
This study presents a Scientometric analysis of the selected published literature on robotics technology applications in the field of libraries, highlighting the progress and development of worldwide research in this area.
The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.
Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.
Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.
Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.
Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.
The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.
(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.
The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.
This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.
The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.
This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.
Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.
The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.
The aim of the study is to understand the transformative impact of ChatGPT on artificial intelligence (AI) research, its applications, implications, challenges and potential to shape future AI trends. The study also seeks to assess the relevance and quality of research output through citation and bibliographic coupling analysis.
This study employed a comprehensive bibliometric analysis using Biblioshiny and VOSviewer to investigate the research trends, influential entities and leading contributors in the domain of AI, focusing on the ChatGPT model.
The analysis revealed a high prevalence of AI-related terms, indicating a significant interest in and engagement with ChatGPT in AI studies and applications. “Nature” and “Thorp H.H.” emerged as the most cited source and author, respectively, while the USA surfaced as the leading contributor in the field.
While the findings provide a comprehensive overview of the ChatGPT research landscape, it is important to note that the conclusions drawn are only as current as the data used.
The study highlights potential collaboration opportunities and signals areas of research that might benefit from increased focus or diversification. It serves as a valuable resource for researchers, practitioners and policymakers for strategic planning and decision-making in AI research, specifically in relation to ChatGPT.
This study is one of the first to provide a comprehensive bibliometric analysis of the ChatGPT research domain, its multidimensional impact and potential. It offers valuable insights for a range of stakeholders in understanding the current landscape and future directions of ChatGPT in AI.
This study aims to provide a more complete picture of blockchain development by combining numerous methodologies with diverse data sources, such as academic papers and news articles. This study displays the developmental status of each subject based on the interrelationships of each topic cluster by analyzing high-frequency keywords extracted from the collected data. Moreover, applying above methodologies will help understanding top research topics, authors, venues, institutes and countries. The differences of blockchain research and new are identified.
To identify and find blockchain development linkages, researchers have used search terms such as co-occurrence, bibliographic coupling, co-citation and co-authorship to help us understand the top research topics, authors, venues, institutes and countries. This study also used text mining analysis to identify blockchain articles' primary concepts and semantic structures.
The findings show the fundamental topics based on each topic cluster's links. While “technology”, “transaction”, “privacy and security”, “environment” and “consensus” were most strongly associated with blockchain in research, “platform”, “big data and cloud”, “network”, “healthcare and business” and “authentication” were closely tied to blockchain news. This article classifies blockchain principles into five patterns: hardware and infrastructure, data, networking, applications and consensus. These statistics helped the authors comprehend the top research topics, authors, venues, publication institutes and countries.
Since Web of Science (WoS) and LexisNexis Academic data are used, the study has few sources. Others advise merging foreign datasets. WoS is one of the world's largest and most-used databases for assessing scientific papers.
This study has several uses and benefits. First, key concept discoveries can help academics understand blockchain research trends so they can prioritize research initiatives. Second, bibliographic coupling links academic papers on blockchain. It helps information seekers search and classify the material. Co-citation analysis results can help researchers identify potential partners and leaders in their field. The network's key organizations or countries should be proactive in discovering, proposing and creating new relationships with other organizations or countries, especially those from the journal network's border, to make the overall network more integrated and linked. Prominent members help recruit new authors to organizations or countries and link them to the co-authorship network. This study also used concept-linking analysis to identify blockchain articles' primary concepts and semantic structures. This may lead to new authors developing research ideas or subjects in primary disciplines of inquiry.
Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.
This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.
The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.
Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.
Cyberbullying has become one of the reasons behind the increase in psychological and medical problems. A need to prevent recurrences of cyberbullying incidents and discourage bullies from further bullying the victims has risen. This problem has attracted the attention of all stakeholders across the globe. Various researchers have developed theories and interventions to detect and stop bullying behavior. Previously, researchers focused on helping victims, but as the times have changed, so has the focus of researchers. This study aims to analyze scientific research articles and review papers to understand the development of the knowledge base on the topic.
This study analyzes the performance of literature on cyberbullying perpetration (CBP) using the widely accepted bibliometric analysis techniques: performance analysis and science mapping. The study is based on a dataset extracted from the Web of Science database. Initially, 2,792 articles between 2007 and 2022 were retrieved, which were filtered down to 441. The filter was based on various criteria, but primarily on CBP. VOSViewer and MS Excel were used to analyze the data. In addition, VOSViewer was used to create “bibliometric citations, co-citations, and co-word maps.”
The findings include publication and citation quantum and trends, the top 20 active countries, the most significant research articles and leading journals in this domain. Major themes or clusters identified were “Cyberbullying and victim behavior,” bullying behavior, adolescents and intervention, “cyberbullying associations,” and “cyberbullying personality associations.”
The study is unique because it analyses research articles based on cyberbullies, whereas past studies explored only the victims' side. Further, the present study used the Web of Science database, whereas most studies use the Scopus database.
This study examines academic librarians' perceptions and attitudes toward Internet of Things (IoT) applications in Hong Kong academic libraries and the problems and possible improvements in using IoT technologies to strengthen library services.
This qualitative research used video conferencing software for semi-structured, one-on-one interviews. Participants were given introductory material about the IoT and asked to complete an interview. The data were analyzed using inductive theme clustering for this study.
The analysis identified three themes: perception about applying IoT technology to the library, problems and improvements in using IoT. Participants were generally optimistic about the potential benefits of IoT for improving library operations and providing personalized services. However, they also expressed concerns about privacy and security, errors and extra efforts for information literacy training. They suggested improvements such as incorporating facial recognition technology, advanced RFID technology and collections identification technology to enhance user experience.
Most studies examined users' views rather than librarians' on IoT applications, which few studies cover, especially in East Asia.
Scientific publishing has recently faced challenges in dealing with questionable (predatory and hijacked) journals. The presence of questionable journals in any field, including management science, will yield junk science. Although there are studies about questionable journals in other fields, these journals have not yet been examined in the field of business and management. This study aims to identify facilitators and barriers to dealing with questionable journals in management science.
A Delphi research method consisting of three rounds was used in this study. Data were collected from 12 experts in the first two rounds, and ten experts in the final round.
The present study shows that management science is vulnerable to questionable journals. A total of 18 barriers and eight facilitators to dealing with questionable journals in management science were found. The present study also identifies some new barriers and facilitators for avoiding questionable journals, which are specific to management science and have not been identified in previous research. Most of these barriers and facilitators were identified as “important” or “very important”. Publishers and scientific databases, government, the research community and universities and research centers were identified as critical players in overcoming challenges posed by questionable journals.
The number of articles that investigate predatory journals in management science is limited, and there is no research focused specifically on hijacked journals in this field. This study identifies facilitators and obstacles to dealing with predatory and hijacked journals in the field of management, by gathering opinions from experts. Thus it is the first study to examine hijacked journals in the field of management science. It is also one of the few studies that examine predatory and hijacked journals by conducting exploratory research rather than with a descriptive/conceptual approach.
In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.
To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.
The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.
This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.
This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.
The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.
Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.
The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.
The purpose of the present study was to identify the conditions that affect the absorptive capacity (AC) of knowledge, discover its consequences and design an AC paradigm pattern in public academic libraries. To do so, AC was investigated at the levels of individual and organizational learning.
In this paper, to answer the three major questions of this qualitative-survey study, the views of 24 experts were examined using an in-depth semi-structured interview and grounded theory strategy. The data were collected using a nonrandom combined targeted sampling procedure (targeted and snowball) and analyzed based on Strauss and Corbin’s (1998) approach and utilizing MAX Qualitative Data Analysis (MAXQDA) software.
Throughout the grounded analytic stages, 121 open codes were identified. These were distributed around the AC axial category in academic libraries in terms of individual and organizational learning. The 33 axial concepts were then categorized into 16 selective general categories. The paradigm pattern was initially designed to explore the relationships between causal, intervening, strategies, context and consequences conditions. The transition from academic libraries to learning organizations, their increased functional value and the development of their innovation were identified as the consequences of AC development.
This qualitative research is the first in the field of Library and Information Science (LIS) to support AC both theoretically and empirically in terms of learning in academic libraries. This study thus not only addresses a key research gap in LIS but also provides significant insights and direction to interested librarians and researchers.
The privacy calculus based on a single stakeholder failed to explain users' co-owned information disclosure owing to the uniqueness of co-owned information. Drawing on collective privacy calculus theory and impression management theory, this study attempts to explore the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective.
Drawing on collective privacy calculus theory and impression management theory, this study explores the co-owned information disclosure of social network platform users from a collective perspective rather than an individual perspective based on a survey of 740 respondents.
This study finds that self-presentation and others presentation directly positively affect users' co-owned information disclosure. Also, self-presentation, others presentation and relationship presentation indirectly positively affect users' co-owned information disclosure via relationship support. Furthermore, personal privacy concern, others' privacy concern and relationship privacy concern indirectly negatively affect users' co-owned information disclosure via relationship risk.
The findings develop the theory of collective privacy calculus and impression management, which offer insights into the design of the collective privacy protection function of social network platform service providers.
This study holistically and systematically consolidates the available research on digital reading to reveal the research trends of the past 20 years. Moreover, it explores the thematic evolution, hotspots and developmental characteristics of digital reading. This study, therefore, has the potential to serve as a research guide to researchers and educators in relevant fields.
The authors applied a bibliometric approach using Derwent Data Analyzer and VOSviewer to retrieve 2,456 publications for 2003–2022 from the Web of Science (WoS) database.
The results revealed that most studies' participants were university students and the experimental methods and questionnaires were preferred in digital reading researches. Among the influential countries or regions, institutions, journals and authors, the United States of America, University of London, Electronic Library and Chen, respectively, accounted for the greatest number of publications. Moreover, the authors identified the developmental characteristics and research trends in the field of digital reading by analyzing the evolution of keywords from 2003–2017 to 2018–2022 and the most frequently cited papers by year. “E-books,” “reading comprehension” and “literacy” were the primary research topics. In addition, “attention,” “motivation,” “cognitive load,” “dyslexia,” “engagement,” “eye-tracking,” “eye movement,” “systematic analysis,” “meta-analysis,” “smartphone” and “mobile reading/learning” were potential new research hotspots.
This study provides valuable insights into the current status, research direction, thematic evolution and developmental characteristics in the field of digital reading. Therefore, it has implications for publishers, researchers, librarians, educators and teachers in the digital reading field.
The evolution from emerging technologies to smart libraries is thoroughly analyzed thematically and bibliometrically in this research study, spanning 2013 through 2022. Finding and analyzing the significant changes, patterns and trends in the subject as they are represented in academic papers is the goal of this research.
Using bibliometric methodologies, this study gathered and examined a large corpus of research papers, conference papers and related material from several academic databases.
Starting with Artificial Intelligence (AI), the Internet of Things (IoT), Big Data (BD), Augmentation Reality/Virtual Reality and Blockchain Technology (BT), the study discusses the advent of new technologies and their effects on libraries. Using bibliometric analysis, this study looks at the evolution of publications over time, the geographic distribution of research and the most active institutions and writers in the area. A thematic analysis is also carried out to pinpoint the critical areas of study and trends in emerging technologies and smart libraries. Some emerging themes are information retrieval, personalized recommendations, intelligent data analytics, connected library spaces, real-time information access, augmented reality/virtual reality applications in libraries and strategies, digital literacy and inclusivity.
This study offers a thorough overview of the research environment by combining bibliometric and thematic analysis, illustrating the development of theories and concepts during the last ten years. The results of this study helps in understanding the trends and future research directions in emerging technologies and smart libraries. This study is an excellent source of information for academics, practitioners and policymakers involved in developing and applying cutting-edge technology in library environments.
The study integrated understanding by design-Internet of Things (UbD-IoT) education with design thinking and computational thinking to plan and design an IoT course. Cross-domain application examples were employed to train students in problem-understanding, deep thinking and logical design for IoT applications.
In this study, the UbD model was integrated with design thinking and computational thinking in the planning and design of an IoT course. The examples of cross-domain applications were used to train students to understand a problem by engaging themselves in deep thinking and helping them think and design logically for an IoT application.
The UbD-IoT learning design greatly decreased students' overall cognitive load. UbD-IoT learning has a significant impact on the performance of computational thinking in problem-solving and problem-understanding. The impact of UbD-IoT learning on logical thinking and program learning cognition in students needs to be verified.
The results of this study have shown that the UbD model is effective in reducing the cognitive load of a learning course and also strengthens T-competencies in the lateral skills of computational thinking, critical problem-solving, logical thinking and creative thinking.
Not all apps can be favored on a smartphone. Users are bound to abandon some if they lose value. Analyzing the reasons behind this phenomenon can help develop strategies for avoiding app abandonment, which is the focus of this study.
This study conducted in-depth open interviews with 47 respondents who had abandoned or deleted mobile apps before and used the qualitative research method grounded theory to analyze the interview content, followed by open coding, axial coding and selective coding.
Seven main reasons are summarized after the relationship between every statement and concept is found and constructed, namely functional value, information expectation, emotional value, platform service, user trust, cost, and social influence.
Based on these findings, this study provided suggestions for mobile app developers to help them effectively improve the user experience and retain their customers.
This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.
Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.
Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.
First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.
The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.
This study presents novel methodologies and important findings for advancing research and practice in MOOCs.
This research aims to analyse the current state of research on the application of Artificial Intelligence (AI) in libraries by examining document type, publication year, keywords, country and research methods. The overarching aim is to enrich the existing knowledge of AI-powered libraries by identifying the prevailing research gaps, providing direction for future research and deepening the understanding needed for effective policy development.
This study used advanced tools such as bibliometric and network analysis, taking the existing literature from the SCOPUS database extending to the year 2022. This study analysed the application of AI in libraries by identifying and selecting relevant keywords, extracting the data from the database, processing the data using advanced bibliometric visualisation tools and presenting and discussing the results. For this comprehensive research, the search strategy was approved by a panel of computer scientists and librarians.
The majority of research concerning the application of AI in libraries has been conducted in the last three years, likely driven by the fourth industrial revolution. Results show that highly cited articles were published by Emerald Group Holdings Ltd. However, the application of AI in libraries is a developing field, and the study highlights the need for more research in areas such as Digital Humanities, Machine Learning, Robotics, Data Mining and Big Data in Academic Libraries.
This study has excluded papers written in languages other than English that address domains beyond libraries, such as medicine, health, education, science and technology.
This article offers insight for managers and policymakers looking to implement AI in libraries. By identifying clusters and themes, the article would empower managers to plan ahead, mitigate potential drawbacks and seize opportunities for sustainable growth.
Previous studies on the application of AI in libraries have taken a broad approach, but this study narrows its focus to research published explicitly in Library and Information Science (LIS) journals. This makes it unique compared to previous research in the field.
Knowledge adoption is the key to effective knowledge exchange in virtual question-and-answer (Q&A) communities. Although previous studies have examined the effects of knowledge content, knowledge source credibility and the personal characteristics of knowledge seekers on knowledge adoption in virtual Q&A communities from a static perspective, the impact of answer deviation on knowledge adoption has rarely been explored from a context-based perspective. The purpose of this study is to explore the impact of two-way deviation on knowledge adoption in virtual Q&A communities, with the aim of expanding the understanding of knowledge exchange and community management.
The same question and the same answerer often yield multiple answers. Knowledge seekers usually read multiple answers to make adoption decisions. The impact of deviations among answers on knowledge seekers' knowledge adoption is critical. From a context-based perspective, a research model of the impact of the deviation of horizontal and vertical answers on knowledge adoption is established based on the heuristic-systematic model (HSM) and empirically examined with 88,287 Q&A data points and answerer data collected from Zhihu. Additionally, the moderation effects of static factors such as answerer reputation and answer length are examined.
The negative binomial regression results show that the content and emotion deviation of horizontal answers negatively affect knowledge seekers' knowledge adoption. The content deviation of vertical answers is negatively associated with knowledge adoption, while the emotion deviation of vertical answers is positively related to knowledge adoption. Moreover, answerer reputation positively moderates the negative effect of the emotion deviation of horizontal answers on knowledge adoption. Answer length weakens the negative correlation between the content deviation of horizontal and vertical answers and knowledge adoption.
This study extends previous research on knowledge adoption from a static perspective to a context-based perspective. Moreover, information deviation is expanded from a one-way variable to a two-way variable. The combined effects of static and contextual factors on knowledge adoption are further uncovered. This study can not only help knowledge seekers identify the best answers but also help virtual Q&A community managers optimize community design and operation to reduce the cost of knowledge search and improve the efficiency of knowledge exchange.
An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.
Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.
DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.
The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.
With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.
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