Purpose This study sought to analyze the correlation between artificial intelligence (AI) and libraries and examine whether there were any shifts in research trends related to these two topics during the coronavirus pandemic. Design/methodology/approach The study gathered secondary data from the Scopus website using the keywords “AI,” “library” and “repository,” from 1993 to 2022. Data were re-analyzed using the bibliometric software VOSviewer to examine the trending country's keyword relations and appearance and Biblioshiny to study the publication metadata. Findings Index keywords, such as “human,” “deep learning,” “machine learning,” “surveys” and “open-source software,” became popular during 2020, being closely related to digital libraries. Additionally, the annual scientific production of papers increased significantly in 2021. Words related to data mining also had the most significant growth from 2019 to 2022 because of the importance of data mining for library services during the pandemic. Practical implications This study provides insight for librarians for the implementation of AI to support repositories during the pandemic. Librarians can learn how to maximize the AI-based repository services in academic libraries during the pandemic. Furthermore, academic libraries can create policies for repository services using AI. Social implications This study can lead researchers, academicians and practitioners in conducting research on AI in library repositories. Originality/value As research on AI and digital repositories remains limited, the study identifies themes and highlights the knowledge gap existing in the field.

Special selection on contemporary digital culture and readingFirst, we thank the publisher for increasing the printing budget by 20 more articles this year and next year in response to the long print queue.Also, we have had a timely response to the COVID-19 pandemic by compiling two special issues (Huang et al., 2021(Huang et al., , 2022)), which have been well-received with significant downloads and citations.Besides, our special selections and editorials (Chiu and Ho, 2021, 2022) better guide readers to an overview of the upcoming contemporary research in various fields covered by Library Hi Tech and related to our recently published articles.To continue our efforts, this issue of Library Hi Tech consists of a selection of papers focusing on digital culture and reading in the current mobile digital era.

PurposeThe digital media recording and broadcasting classroom using Internet real-time intelligent image positioning and opinion monitoring in communication teaching is researched and analyzed.Design/methodology/approachFirst, spatial grid positioning and monitoring and image intelligent recognition technologies were used to extract and analyze teaching images by mastering Internet of Things (IoT) technology and establishing an intelligent image positioning and opinion monitoring digital media recording and broadcasting system framework. Next, a positioning node algorithm was utilized to measure the image distance, and then a moving node location model under the IoT was established. In addition, a radial basis function (RBF) neural network was used to realize the signal transmission function. The experimental data of the adopted RBF based on the optimization of the adaptive cuckoo search (ACS-RBF) neural network, particle swarm algorithm neural network, and method of least squares optimization were compared and analyzed. In addition, a more efficient RBF neural network was adopted. Finally, the digital media recording and broadcasting classroom scheme of real-time intelligent image positioning and opinion monitoring was designed. In addition, the application environment of digital media actual teacher teaching was detected, and recording and broadcasting pictures were analyzed and researched.FindingsThe actual value, predicted value, and the number of predicted samples of the ACS-RBF model were all better than those of the two other neural networks. According to the analysis and comparison of the sampling optimization Monte Carlo localization (SOMCL), Monte Carlo, and genetic algorithm optimization-based Monte Carlo positioning algorithms, the SOMCL algorithm showed better robustness, and its positioning efficiency was superior to that of the two other algorithms. In addition, the SOMCL algorithm greatly reduced the positioning and monitoring energy consumption.Originality/valueThe application of real-time intelligent image positioning and monitoring technology in actual communication teaching was realized in the study.

PurposeAlthough MOOCs have become a pervasive online learning model, the problem of high dropout rates still persists. Gathering the reasons for the high dropout rate can help to improve the platform design and management of the MOOCs.Design/methodology/approachA total of 74 studies was extracted from the Web of Science and Scopus. Following the PRISMA (Preferred Reporting Items for systematic Reviews and Meta-Analyses) guidelines, the open-source program CiteSpace is employed to review and induce the studies on the antecedents of MOOC dropout.FindingsThe antecedents of the MOOC dropout rate are the psychological, social, personal, course-related, and time factors, and the unexpected hidden cost. Motivation and interaction, which have a decisive impact on the dropout rate of MOOCs, interact with each other. Interaction helps to strengthen the motivation, and appropriate course design enhances the degree of interaction.Originality/valueFrom the perspective of a learner, the more knowledge and skills the learners acquire, the more likely they will complete the course. Possessing adequate foundational knowledge is one way to arrest the dropout rate. On the part of the MOOC platform, better course design eases the dropout rate. Further, the course duration and hidden cost in MOOCs contribute to the dropout rate.

PurposeThe purpose of this study is to identify the factors leading to adoption of social networking sites (SNS) by librarians of entrepreneurial libraries to provide support to business community in their market research. Identification of these adoption factors will help to design SNS in the way that led to their seamless diffusion in university libraries, making them capable of providing reliable and useful market knowledge to the business community.Design/methodology/approachTo meet the research objectives, this study is based on the technology adoption model (TAM). The survey was conducted with 50 librarians of various universities across Europe, Asia, America, Africa and Australia, sampled through non probabilistic sampling techniques-purposive and snowball sampling. The collected data are analyzed through partial least squares structural equation modeling (PLS-SEM) to estimate the formulated structural equation model using SmartPLS 3.2.9 software version.FindingsThe results indicate that university librarians have high perception about usefulness and ease of using SNS for meeting their business objectives. The perceived usefulness (PU) is solely based on the prolonged adoption of SNS technology in personal lives of the librarians. The perceived ease of use (PEU) is triggered by the increasing quality of evolving SNS technology, especially user friendliness. The prolonged use of technology, increasing market entry of brands and improving quality of SNS will overcome librarian resistance to use SNS for market research related support to business community. Evolving technologies are more likely to be adopted seamlessly in entrepreneurial libraries.Research limitations/implicationsThis study has implications for librarians, entrepreneurs and technology providers. Librarians should have flexible technology adoption policies in place to keep technology adoption synchronized with changing market conditions. Entrepreneurs should share clear and accurate business needs as well as practicing ethical and lawful business practices. They should also help libraries to gain new skills to make future adoption easier. Technology providers should focus on improving the quality of their technology by increasing their functional utilities, performance and reliability as well as making them more user-friendly.Originality/valueThis study investigates technology adoption of SNS in libraries from the perspective of providing market research related services to entrepreneurs rather than just digitalizing library services for student users.

PurposeThis study aims to identify the developer’s objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations.Design/methodology/approachIn this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004–2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies.FindingsThe outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers’ challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers; (2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques); (3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context; (4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques; (5) performing a SWOT analysis on the selected studies; (6) based on the developer’s challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context.Research limitations/implicationsThis study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can overcome the identified challenges (including mashup-oriented API, Time-consuming process, learn how to use the API, integrated problem, API method usage location and limited usage of code) in the future by proposing a framework or recommendation system. Furthermore, the classification of current state-of-the-art API recommendation techniques also helps the researchers who wish to work in the future in the context of API recommendation.Practical implicationsThis study not only facilitates the researcher but also facilitates the practitioners in several ways. The current study guides the developer in minimizing the development time in terms of selecting relevant APIs rather than following traditional manual selection. Moreover, this study facilitates integrating APIs in a project. Thus, the recommendation system saves the time for developers, and increases their productivity.Originality/valueAPI recommendation remains an active area of research in web and mobile-based applications development. The authors believe that this study acts as a useful tool for the interested researchers and practitioners as it will contribute to the body of knowledge in API recommendations context.

PurposeThis study aims to provide a systematic and complete knowledge map for use by researchers working in the field of research data. Additionally, the aim is to help them quickly understand the authors' collaboration characteristics, institutional collaboration characteristics, trending research topics, evolutionary trends and research frontiers of scholars from the perspective of library informatics.Design/methodology/approachThe authors adopt the bibliometric method, and with the help of bibliometric analysis software CiteSpace and VOSviewer, quantitatively analyze the retrieved literature data. The analysis results are presented in the form of tables and visualization maps in this paper.FindingsThe research results from this study show that collaboration between scholars and institutions is weak. It also identified the current hotspots in the field of research data, these being: data literacy education, research data sharing, data integration management and joint library cataloguing and data research support services, among others. The important dimensions to consider for future research are the library's participation in a trans-organizational and trans-stage integration of research data, functional improvement of a research data sharing platform, practice of data literacy education methods and models, and improvement of research data service quality.Originality/valuePrevious literature reviews on research data are qualitative studies, while few are quantitative studies. Therefore, this paper uses quantitative research methods, such as bibliometrics, data mining and knowledge map, to reveal the research progress and trend systematically and intuitively on the research data topic based on published literature, and to provide a reference for the further study of this topic in the future.

PurposeThe purpose of this paper is to present a comprehensive analysis of the current status and development of the digital journalism field from 1987 to 2021 using the Dimensions database.Design/methodology/approachUsing the Dimensions.ai database, 1734 articles were identified through search strategies which were published from 1987 to 2021. The downloaded results were analysed using specific parameters with the help of bibliometric and science mapping tools: Biblioshiny, VOSviewer and CiteSpace. The key contributions of the present comprehensive bibliometric study of the digital journalism field can be seen in terms of the following aspects: (1) Publication analysis from the perspectives of publication growth, key journals, contributing authors, institutions and countries done through Biblioshiny package. (2) Citation network analysis from the perspective of co-citation structure of papers, authors, countries and institutions done through VOSviewer. (3) Timeline analysis and keywords burst detection to identify hotspots and research trends in digital journalism with the help of CiteSpace.FindingsThe first paper with the keyword digital journalism was published in the year 1989. From 2011 onwards, there has been growth in digital journalism literature. The most popular journal in digital journalism studies is Digital Journalism, Journalism, Journalism Practice, Journalism Studies. Lewis, S.C. has contributed the most number of papers in digital journalism. Further, authors from the countries the USA, Spain, Brazil and UK have contributed immensely. The citation network of authors, institutions and countries contributing to digital journalism studies has also been explored in the study. Through burst analysis, hot topics in digital journalism were identified.Originality/valueThe paper provides a complete overview of the growth of digital journalism literature published from 1987 to 2021. The originality of this work lies in the triangulation of Biblioshiny, VOSviewer and CiteSpace software to present various aspects of bibliometric study. Findings of the study can help the researchers to identify areas as well as journals, authors, institutions working actively in the field of digital journalism.

PurposeGood abandonment behavior refers to users obtaining direct answers via search engine results pages (SERPs) without clicking any search result, which occurs commonly in mobile search. This study aims to better understand users' good abandonment behavior and perception, and then construct a good abandonment prediction model for mobile search with improved performance.Design/methodology/approachIn this study, an in situ user mobile search experiment (N = 43) and a crowdsourcing survey (N = 1,379) were conducted. Good abandonment behavior was analyzed from a quantitative perspective, exploring users' search behavior characteristics from four aspects: session and query, SERPs, gestures and eye-tracking data.FindingsUsers show less engagement with SERPs in good abandonment, spending less time and using fewer gestures, and they pay more visual attention to answer-like results. It was also found that good abandonment behavior is often related to users' perceived difficulty of the searching tasks and trustworthiness in the search engine. A good abandonment prediction model in mobile search was constructed with a high accuracy (97.14%).Originality/valueThis study is the first to explore eye-tracking characteristics of users' good abandonment behavior in mobile search, and to explore users' perception of their good abandonment behavior. Visual attention features are introduced into good abandonment prediction in mobile search for the first time and proved to be important predictors in the proposed model.

Purpose In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Design/methodology/approach In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Findings In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing. Originality/value In order to analyze the text complexity of Chinese and foreign academic English writings, the artificial neural network (ANN) under deep learning (DL) is applied to the study of text complexity. Firstly, the research status and existing problems of text complexity are introduced based on DL. Secondly, based on Back Propagation Neural Network (BPNN) algorithm, analyzation is made on the text complexity of Chinese and foreign academic English writings. And the research establishes a BPNN syntactic complexity evaluation system. Thirdly, MATLAB2013b is used for simulation analysis of the model. The proposed model algorithm BPANN is compared with other classical algorithms, and the weight value of each index and the model training effect are further analyzed by statistical methods. Finally, L2 Syntactic Complexity Analyzer (L2SCA) is used to calculate the syntactic complexity of the two libraries, and Mann–Whitney U test is used to compare the syntactic complexity of Chinese English learners and native English speakers. The experimental results show that compared with the shallow neural network, the deep neural network algorithm has more hidden layers and richer features, and better performance of feature extraction. BPNN algorithm shows excellent performance in the training process, and the actual output value is very close to the expected value. Meantime, the error of sample test is analyzed, and it is found that the evaluation error of BPNN algorithm is less than 1.8%, of high accuracy. However, there are significant differences in grammatical complexity among students with different English writing proficiency. Some measurement methods cannot effectively reflect the types and characteristics of written language, or may have a negative relationship with writing quality. In addition, the research also finds that the measurement of syntactic complexity is more sensitive to the language ability of writing. Therefore, BPNN algorithm can effectively analyze the text complexity of academic English writing. The results of the research provide reference for improving the evaluation system of text complexity of academic paper writing.