Information Discovery and Delivery

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  • Exploring the mobile learning needs amongst performing arts students

    Purpose: This paper aims to study the information needs and online information-seeking behaviors on mobile platforms of performing arts students at a college level. Design/methodology/approach: Survey instruments were used to collect data from performing arts students at the Hong Kong Academy of Performing Arts (HKAPA), a metropolitan’s major performing arts tertiary institution. Data collected were analyzed through descriptive statistics and other statistical methods, and the music-related students were compared with the production-related students. Findings: The result reveals that performing arts students all owned their mobile devices and often used mobile apps for non-academic purposes, but they did not often use mobile library services or read online academic contents with their mobile devices. The participants considered inadequate signal coverage, slow loading time, difficulty in reading on a mobile device and the lack of specialized mobile apps as more significant barriers affecting their usage. There are some significant differences between the music-related and production-related student groups in that music-related students watched lectures on the library websites and used electronic music scores more often than the production-related students. Practical implications: This study contributes to the input for enhancements and policies to future mobile services and facilities of performing art libraries. Originality/value: There have been scant studies on the mobile learning needs of performing arts students, especially in Asia.

  • Guest editorial
  • A classification model for prediction of clinical severity level using qSOFA medical score

    Purpose: The purpose of this study is to develop an efficient prediction model using vital signs and standard medical score systems, which predicts the clinical severity level of the patient in advance based on the quick sequential organ failure assessment (qSOFA) medical score method. Design/methodology/approach: To predict the clinical severity level of the patient in advance, the authors have formulated a training dataset that is constructed based on the qSOFA medical score method. Further, along with the multiple vital signs, different standard medical scores and their correlation features are used to build and improve the accuracy of the prediction model. It is made sure that the constructed training set is suitable for the severity level prediction because the formulated dataset has different clusters each corresponding to different severity levels according to qSOFA score. Findings: From the experimental result, it is found that the inclusion of the standard medical scores and their correlation along with multiple vital signs improves the accuracy of the clinical severity level prediction model. In addition, the authors showed that the training dataset formulated from the temporal data (which includes vital signs and medical scores) based on the qSOFA medical scoring system has the clusters which correspond to each severity level in qSOFA score. Finally, it is found that RAndom k-labELsets multi-label classification performs better prediction of severity level compared to neural network-based multi-label classification. Originality/value: This paper helps in identifying patient' clinical status.

  • A study of the influencing factors of mobile social media fatigue behavior based on the grounded theory

    Purpose: This paper aims to discuss major influencing factors causing users’ mobile social media fatigue and divides them into three hierarchies, including causal factors, intermediary factors and outcome factors. The study also sorts out connections between different levels of factors, thus providing effective guidance for the sustained development of social media. Design/methodology/approach: Based on the grounded theory and by collecting data through in-depth interviews, the authors use open coding, axial coding and selective coding to analyze major influencing factors of users’ mobile social media fatigue, build a model using the software NVivo 11, organize and analyze mobile social media fatigue behavior and identify the relationships by combining the interpretive structural model and explore connections among the factors. Findings: The influencing factors of mobile social media fatigue behavior conform with the stressors-strains-outcomes (SSO) theoretical framework, where stressors (S) include the five factors of fear of missing out, perceived overload, compulsive use, time cost and privacy concerns; strains (S) include the five factors of a low sense of achievement, emotional anxiety, reduced interest, social concerns and emotional exhaustion; outcomes (O) include the six factors of neglect behavior, diving behavior, avoidance behavior, tolerance behavior, withdrawal behavior and substitution behavior. Research limitations/implications: It focuses on the discussion of the interactions between users’ stressors, strains and outcomes without fully considering the impact of social environment and educational background on social media fatigue behavior. This study only focuses on one social media platform in the Chinese context, namely, WeChat. We reply on the qualitative research method to construct the relationships between social media fatigue factors because we were mainly interested in how users would respond psychologically and emotionally to social media fatigue behavior. Practical implications: The study has extended the application of the SSO theory. Additionally, the research method and model used in this paper may serve as guidelines to other interested scholars who intend to explore relevant variables and conduct further research on the influencing factors of social media fatigue. In analyzing the causality of social media fatigue, the study has integrated the intermediary factor strain to display users’ strains from social media stress with a more detailed path discussion on the causality of social media fatigue, which has not received broad attention in previous research literature on social networking services users’ use. Social implications: In this study, text data are collected in a diversity of forms combined, allowing respondents to answer questions without being limited by the questions in the questionnaire, which helped us to identify new variables of social media fatigue. As a result, we were able to dig out the fundamental causes of social media fatigue and potential connections between the factors. Relevant scholars, users and businesses may analyze, manage and forecast users’ social media fatigue behavior by analyzing the type of social media stress and users’ state, providing guidance for the proposal of corresponding management strategies. Originality/value: Most relevant studies focus on the sustained use of social media, and there is a scarcity of studies on social media fatigue in China. There is very limited research that conducts model analysis of social media fatigue through the integration of stressors, strains and outcomes.

  • Cognitive needs and use of social media: a comparative study of gratifications sought and gratification obtained

    Purpose: The purpose of this study is to compare the gratification sought and gratification obtained for cognitive needs from social media among information professionals in the limelight of uses and gratification theory. Cognitive needs are related to knowledge, acquiring information, comprehension etc., and gratification sought and gratification obtained are two distinct components of the uses and gratification theory. Design/methodology/approach: For this quantitative research study, a self-administered survey questionnaire was used to collect data from the participants of the study. Sample of this study was 700 information professionals who are necessarily users of social media. Findings: Finding of this study depicted that gratification obtained and gratification sought from social media for cognitive needs are different from each other, and information professionals need to revisit their social media use for cognitive needs. Research limitations/implications: The present study is limited to gratification sought and gratification obtained for cognitive needs among information professionals. Practical implications: This study has determined that information professionals need to revisit their social media use for cognitive needs, as the obtained gratifications are different from gratification sought from social media. Social implications: Social media provides versatility of information in different forms and large numbers of information professionals are the users of social media around globe. Perceived use of social media for cognitive needs has been resulted into destructed gratifications. This study has brought the actual outcome of the use of social media to the audience so that they may rectify their social media use. Originality/value: This study is a significant contribution for information professionals to review the gratifications sought and obtained from social media for cognitive needs. It has been established in this study that gratifications sought are significantly different from gratifications obtained from social media among information professionals.

  • An effective foreground segmentation using adaptive region based background modelling

    Purpose: Background modelling has played an imperative role in the moving object detection as the progress of foreground extraction during video analysis and surveillance in many real-time applications. It is usually done by background subtraction. This method is uprightly based on a mathematical model with a fixed feature as a static background, where the background image is fixed with the foreground object running over it. Usually, this image is taken as the background model and is compared against every new frame of the input video sequence. In this paper, the authors presented a renewed background modelling method for foreground segmentation. The principal objective of the work is to perform the foreground object detection only in the premeditated region of interest (ROI). The ROI is calculated using the proposed algorithm reducing and raising by half (RRH). In this algorithm, the coordinate of a circle with the frame width as the diameter is considered for traversal to find the pixel difference. The change in the pixel intensity is considered to be the foreground object and the position of it is determined based on the pixel location. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; The proposed system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizes the pixel as the foreground and mines the precise foreground object. The broad experimental results and the evaluation parameters of the proposed approach with the state of art methods were compared against the most recent background subtraction approaches. Moreover, the efficiency of the authors’ method is analyzed in different situations to prove that this method is available for real-time videos as well as videos available in the 2014 challenge change detection data set. Design/methodology/approach: In this paper, the authors presented a fresh background modelling method for foreground segmentation. The main objective of the work is to perform the foreground object detection only on the premeditated ROI. The region for foreground extraction is calculated using proposed RRH algorithm. Most of the techniques study their updates to the pixels of the complete frame which may result in increased false rate; most challenging case is that, the slow moving object is updated quickly to detect the foreground region. The anticipated system deals these flaw by controlling the ROI object (the region only where the background subtraction is performed) and thus extracts a correct foreground by exactly categorizing the pixel as the foreground and mining the precise foreground object. Findings: Plum Analytics provide a new conduit for documenting and contextualizing the public impact and reach of research within digitally networked environments. While limitations are notable, the metrics promoted through the platform can be used to build a more comprehensive view of research impact. Originality/value: The algorithm used in the work was proposed by the authors and are used for experimental evaluations.

  • Feature mining and analysis of gray privacy products

    Purpose: Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products. Design/methodology/approach: This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear. Findings: Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions. Originality/value: The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.

  • Weave coding into K-5 curricula as new literacies

    Purpose: The purpose of this paper is to posit that coding should be considered as a critical part of new literacies. Teacher educators should first adopt the new literacies perspective, and then prepare pre-service teachers to teach both traditional literacy and new literacies skills, especially preparing them how to weave coding into K-5 literacy curricula to cultivate younger learners’ new ways of expressions and computational thinking skills. To facilitate this educational transformation, low-cost Web 2.0 tools and apps were introduced as one practical approach, along with some literacy lesson ideas to help teacher educators and pre-service teachers begin to integrate coding into the K-5 literacy curricula. Design/methodology/approach: This paper is a viewpoint paper. Findings: A table of low-cost Web 2.0 tools was presented with sample lesson ideas. Originality/value: More than ever, coding breaks the traditional definition of literacy as paper-based reading and writing. It empowers students to read, write and create with multimodality on multiple platforms. Weaving coding into the literacy curricula offers the window to promote both computational thinking and new literacies skills. Teacher educators, among all other stakeholders, should begin the efforts to prepare pre-service teachers to weave coding into the literacy curricula and other content areas in the teacher educations programs now.

  • Do altmetrics correlate with citations? A study based on the 1,000 most-cited articles

    Purpose: The purpose of this paper is to study the presence of highly cited papers of Nature in social media websites and tools. It also tries to examine the correlation between altmetric and bibliometric indicators. Design/methodology/approach: This descriptive study was carried out using altmetric indicators. The research sample consisted of 1,000 most-cited articles in Nature. In February 2019, the bibliographic information of these articles was extracted from the Scopus database. Then, the titles of all articles were manually searched on Google, and by referring to the article in the journal website and altmetric institution, the data related to social media presence and altmetric score of articles were collected. The data were analyzed using Microsoft Excel and SPSS. Findings: According to the results of the study, from 1,000 articles, 989 of them (98.9 per cent) were mentioned at least once in different social media websites and tools. The most used altmetric source in highly cited articles was Mendeley (98.9 per cent), followed by Citeulike (79.8 per cent) and Wikipedia (69.4 per cent). Most Tweets, blog posts, Facebook posts, news stories, readers in Mendeley, Citeulike and Connotea and Wikipedia citations belonged to the article titled “Mastering the game of Go with deep neural networks and tree search”. The highest altmetric score was 3,135 which belonged to this paper. Most tweeters and articles’ readers were from the USA. The membership type of the tweeters was public membership. In terms of fields of study, most readers were PhD students in Agricultural and Biological Sciences. Finally, the results of Spearman’s Correlation revealed positive significant statistical correlation between all altmetric indicators and received citations of highly cited articles (p-value = 0.0001). Practical implications: The results of this study can help researchers, editors and editorial boards of journals better understand the importance and benefits of using social media and tools to publish articles. Originality/value: Altmetrics is a relatively new field, and in particular, there are not many studies related to the presence of articles in various social media until now. Accordingly, in this study, a comprehensive altmetric analysis was carried out on 1000 most-cited articles of one of the world's most reliable journals.

  • Is learning anytime, anywhere a good strategy for success? Identifying successful spatial-temporal patterns of on-the-job and full-time students

    Purpose: Online learning is well-known by its flexibility of learning anytime and anywhere. However, how behavioral patterns tied to learning anytime and anywhere influence learning outcomes are still unknown. Design/methodology/approach: This study proposed concepts of time and location entropy to depict students’ spatial-temporal patterns. A total of 5,221 students with 1,797,677 logs, including 485 on-the-job students and 4,736 full-time students, were analyzed to depict their spatial-temporal learning patterns, including the relationships between identified patterns and students’ learning performance. Findings: Analysis results indicate on-the-job students took more advantage of anytime, anywhere than full-time students. Students with a higher tendency for learning anytime and a lower level of learning anywhere were more likely to have better outcomes. Gender did not show consistent findings on students’ spatial-temporal patterns, but partial findings could be supported by evidence in neural science or by cultural and geographical differences. Research limitations/implications: A more accurate approach for categorizing position and location might be considered. Some findings need more studies for further validation. Finally, future research can consider connections between other well-known performance predictors (such as financial situation, motivation, personality and major) and the type of learning patterns. Practical implications: The findings gained from this study can help improve the understandings of students’ learning behavioral patterns and design as well as implement better online education programs. Originality/value: This study proposed concepts of time and location entropy to identify successful spatial-temporal patterns of on-the-job and full-time students.

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