Search results

The purpose of this study was to present a pilot electronic portfolio (e-portfolio)-integrated learning environment by integrating library resources into an e-portfolio system for its application, and to explore reader’s satisfaction of the integrated system.

This study develops a research model by modifying the information success model to explore reader satisfaction to the understanding of the adoption of integrated system. The sample consisted of 289 graduate and undergraduate students. In total, 189 were considered useful and used for analysis. A regression analysis was then conducted to identify key causal relationships.

The findings show that reader-perceived benefits, information quality and system quality are critical factors for the reader’s satisfaction. Overall, the model explained 84 per cent of the variance in reader satisfaction. Thus, the results show that the proposed model does satisfactorily explain the reader’s satisfaction of the integrated system.

There is scant research available in the literature on user satisfaction of pilot e-portfolio-integrated learning environment from a reader perspective. The findings of this research provide some useful insights into a reader’s satisfaction toward adoption of the integrated system. In addition, it will be valuable for better understanding of factors affecting the determinants of reader’s satisfaction, which improve the reader’s satisfaction of the integrated system and thereby boost realization of collaborative learning environment.

The purpose of this paper is to assess the value of Goodreads reader ratings for measuring the wider impact of scholarly books published in the field of History.

Book titles were extracted from the reference lists of articles that appeared in 604 history journals indexed in Scopus (2007-2011). The titles were cleaned and matched with WorldCat.org (for publisher information) as well as Goodreads (for reader ratings) using an API. A set of 8,538 books was first filtered based on Dewey Decimal Classification class 900 “History and Geography”, then a subset of 997 books with the highest citations and reader ratings (i.e. top 25 per cent) was analysed separately based on additional characteristics.

A weak correlation (0.212) was found between citation counts and reader rating counts for the full data set (n=8,538). An additional correlation for the subset of 997 books indicated a similar weak correlation (0.190). Further correlations between citations, reader ratings, written reviews, and library holdings indicate that a reader rating on Goodreads was more likely to be given to a book held in an international library, including both public and academic libraries.

Research on altmetrics has focused almost exclusively on scientific journal articles appearing on social media services (e.g. Twitter, Facebook). In this paper we show the potential of Goodreads reader ratings to identify the impact of books beyond academia. As a unique altmetric data source, Goodreads can allow scholarly authors from the social sciences and humanities to measure the wider impact of their books.

This study aims to investigate the influences of task-technology fit on university students’ attitudes towards ubiquitous library-supported learning when they use a mobile library app, Line@Library.

In this study, structural equation modelling to examine 158 valid questionnaires are used. The study aims to examine the effects of task-technology fit (TTF) on university students’ attitudes towards mobile learning (AML) when using Line@Library.

The results show that task-technology fit is an important role that influences the students’ attitudes towards mobile learning. The factor “technology characteristics” is considered when the students attempted to use the mobile app to solve problems or complete tasks. This study also found that the students responded with positive perceptions of the task-technology fit and had positive perceptions of its ease of use. Furthermore, usefulness, ease of use and affection of AML were found to be the most influential predictors of mobile library adoption intention.

From the perspective of learners, this study investigates the relationships of the combination of social media and a mobile library between TTF and AML. This study further found that not only ease of use, usefulness and affection but also task-technology fit can be a predictor that influences students’ attitudes towards mobile learning.

The purpose of this research is to illustrate the use of artificial neural network (ANN) and data‐mining (DM) technologies as a good approach for satisfying the requirements of library users.

This research presents the Intelligent Library Materials Recommendations System (ILMRS) which uses the adaptive resonance theory (ART) network to distribute readers into different clusters according to their personal background. When clusters of related personal background have been established, the Apriori algorithm is used to discover the suitable materials in which readers are interested and which they may need.

The investigation results indicate that the ART and Apriori mining techniques can be used to improve the accuracy of the recommendations for reading materials in the library. Moreover, readers can be divided by means of demographic variables into three segments. Finally, the questionnaire survey proved that the proposed recommender system will be a suitable approach for stimulating the readers' motivation and interest. Research limitations/implications – This research is limited by its datasets from a digital library of a university in Taiwan, and it is applied by ART and Apriori mining techniques to recommend materials of readers.

Today, digital information is becoming ever more popular. The huge quantity and the diversity of digital information are its main features. Therefore, readers are interested in obtaining useful information in an efficient manner. In this research, a digital library can use this approach to anticipate a reader's needs in advance based on the mining results.

Nowadays, database management system has been applied in library management, and a great number of data about readers’ visiting history to resources have been accumulated by libraries. A lot of important information is concealed behind such data. The purpose of this paper is to use a typical data mining (DM) technology named an association rule mining model to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Association rule mining algorithm is applied to find out borrowing rules of readers according to their borrowing records, and to recommend other booklists for them in a personalized way, so as to increase utilization rate of data resources at library.

Through an analysis on record of book borrowing by readers, library manager can recommend books that may be interested by a reader based on historical borrowing records or current book-borrowing records of the reader.

If many different categories of book-borrowing problems are involved, it will result in large length of encoding as well as giant searching space. Therefore, future research work may be considered in the following aspects: introduce clustering method; and apply association rule mining method to procurement of book resources and layout of books.

The paper provides a helpful inspiration for Big Data mining and software development, which will improve their efficiency and insight on users’ behavior and psychology.

The paper proposes a framework to help users understand others’ behavior, which will aid them better take part in group and community with more contribution and delightedness.

DM technology has been used to discover information concealed behind Big Data in library; the library personalized recommendation problem has been analyzed and formulated deeply; and a method of improved association rules combined with artificial bee colony algorithm has been presented.

The purpose of this paper is to evaluate the performance of the multi-source book review system (MBRS). MBRS was designed to reduce information overload using the internet and to accommodate different learner preferences.

The authors experimentally compared MBRS with the Google search engine. MBRS first gathers reviews from online sources, such as bookstores and blogs. It reduces information overload through an advanced filtering and sorting algorithm and by providing a uniform user interface. MBRS accommodates different learning styles through various sort options and through adding video-mediated reviews.

Results indicate that, compared with Google, MBRS: reduces the information overload associated with searching for online book reviews; increases users finding satisfactory book reviews; and allows users to find reviews more quickly. In addition, more than half of the participants found video-mediated book reviews more appealing than traditional text-based reviews.

Future studies might examine the effects of other recommendations or sorting methods to fit individual preferences in a more dynamic way.

This study assisted readers with a preference for visual information in locating reviews of personal interest in less time and with finding reviews more aligned with their individual learning preferences.

This study documents an innovative web site featuring video-mediated book reviews and other mechanisms to accommodate individual preferences. Search engine designers could integrate book reviews with different media types to reduce cognitive load allowing readers to focus attention on the reading task. Internet booksellers or library staff may use this as an effective means to enhance reading motivation.

Readers articulate mood in deeply subjective ways, yet the underlying structure of users' understanding of the media they consume has important implications for retrieval and access. User articulations might at first seem too idiosyncratic, but organizing them meaningfully has considerable potential to provide a better searching experience for all involved. The current study develops mood categories inductively for fiction organization and retrieval in information systems.

The authors developed and distributed an open-ended survey to 76 fiction readers to understand their preferences with regard to the affective elements in fiction. From the fiction reader responses, the research team identified 161 mood terms and used them for further categorization.

The inductive approach resulted in 30 categories, including angry, cozy, dark and nostalgic. Results include three overlapping mood families: Emotion, Tone/Narrative, and Atmosphere/Setting, which in turn relate to structures that connect reader-generated data with conceptual frameworks in previous studies.

The inherent complexity of “mood” should not dissuade researchers from carefully investigating users' preferences in this regard. Adding to the existing efforts of classifying moods conducted by experts, the current study presents mood terms provided by actual end-users when describing different moods in fiction. This study offers a useful roadmap for creating taxonomies for retrieval and description, as well as structures derived from user-provided terms that ultimately have the potential to improve user experience.

Moves towards more interactive services on the web have led libraries to add an increasing range of functionality to their OPACS. Given the prevalence of recommender systems on the wider web, especially in e‐commerce environments, this paper aims to review current research in this area that is of particular relevance to the library community. It attempts to gauge the uptake of recommender systems in exiting OPAC services, and identify issues that might be responsible for inhibiting wider uptake.

This paper draws on an extensive literature review, as well as original research comparing the functionality of 211 public and 118 university library OPACs in the UK. Examining current recommender systems research, it outlines the most significant recommendation models and reviews research in two key areas of recommender systems design: data acquisition, and the explanation of recommendations. It discusses three existing library recommendation systems: BibTip, LibraryThing for Libraries and the in‐house system at the University of Huddersfield.

The authors' analysis indicates that the incorporation of recommender systems into library services is extremely low, with only 2 per cent of public libraries and 11 per cent of university libraries in the UK offering the feature. While system limitations and budget constraints are perhaps partly to blame, it is suggested that library professionals have perhaps yet to be persuaded that the value of recommendations to library users is great enough to warrant their inclusion becoming a priority.

This paper represents the first study of UK library OPACs to focus on the prevalence of recommender systems.

Most academic libraries provide book recommendation services to enable readers to recommend books to the libraries. To facilitate decision-making in book acquisition, this study aimed to develop a method to determine the ranking of the recommended books based on the recommender network.

The recommender network was conducted to establish relationships among book recommenders and their similar readers by using circulation records. Furthermore, social computing techniques were used to evaluate the degree of representativeness of the recommenders and subsequently applied as a criterion to rank the recommended books. Empirical studies were performed to demonstrate the effectiveness of the proposed ranking system. The Spearman’s correlation coefficients between the proposed ranking system and the ranking obtained using reader circulation statistics were used as performance measure.

The ranking calculated using the proposed ranking mechanism was highly and moderately correlated to the ranking obtained using reader circulation statistics. The ranking of recommended books by the librarians was moderately and poorly correlated to the ranking calculated using reader circulation statistics.

The book recommender can be used to improve the accuracy of book recommendations.

This study is the first that considers the recommender network on library book acquisition. The results also show that the proposed ranking mechanism can facilitate effective book-acquisition decisions in libraries.

Lending novels is the major service provided by public libraries. The efforts in developing search systems have been focused on retrieving non-fiction. There is a need for designing systems to support fiction searching in libraries. The aim of this study is to analyze readers’ methods of accessing fiction in a public library for informing the design of fiction search systems. This study seeks to find out which attributes of books readers perceive as indicators of a good novel, and what kind of tactics they use for finding these good novels in the public library.

The authors observed 16 adult library users by semi-structured interviews eliciting information about their literary competence, what characterizes a good novel and how they accessed and identified good novels in the library.

Based on the data this paper developed a tentative reader typology, which differentiated the attributes of good novels and major tactics for accessing them.

The typology was used for inferring user models and design ideas for systems supporting fiction searching.

This is the first empirical study to inform how readers’ literary competence is associated with the tactics used and indicators recognized in books for finding and selecting good novels to borrow.