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The primary purpose of this study was to check the online information retrieval self-efficacy among library professionals in predicting the satisfaction of patrons within universities’ libraries.

The study was grounded on Bandura four sources of self-efficacy, encompassing mastery experience, vicarious experience, social persuasion and physiological states. To accomplish this, a meticulously designed questionnaire was administered to collect data from library professionals employed in universities libraries recognized by the Higher Education Commission in Punjab and capital city of Pakistan (Islamabad). Following by the validation of assumptions, researchers conducted a multiple linear regression test to predict the outcomes of the dependent variable by using the independents variables. Additionally, a comparative evaluation was carried out among all the independent variables to determine their respective contributions to satisfaction of library patrons.

The results emphasized the distinct and substantial significance of three variables, physiological states, social feedback and mastery experience in predicting the satisfaction of library patrons. Nevertheless, vicarious experience did not demonstrate a significant influence on the satisfaction of library patrons. Furthermore, influence of physiological states on the improvement of library patrons’ satisfaction was relatively higher compared to other three self-efficacy sources. In conclusion, research established the essential role of online information retrieval self-efficacy in enhancing the satisfaction of library patrons.

The findings of the study can form a solid basis for devising academic programs to train the library professionals for effective utilization of various information systems and databases. These programs play an important role in improving the self-efficacy of library professionals, ultimately refining their skills in online information retrieval.

In essence, this study provides insights into the factors which are pivotal in effective information searching process, ultimately leading to increase the satisfaction level of library patrons which has not been previously researched in Pakistan as well as the world context. Moreover, the study significance lies in contribute to academic discourse, its potential to transform and promote the library services and as well as empower library professionals in delivering the satisfying and efficient experience for library patrons in the current digital age.

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

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.

The purpose of this study was a longitudinal study on knowledge organization (KO) realm structure and cluster concepts and emerging KO events based on co-occurrence analysis.

This longitudinal study uses the co-occurrence analysis. This research population includes keywords of articles indexed in the Web of Science Core Collection 1975–1999 and 2000–2018. Hierarchical clustering, multidimensional scaling and co-occurrence analysis were used to conduct the present research. SPSS, UCINET, VOSviewer and NetDraw were used to analyze and visualize data.

The “Information Technology” in 1975–1999 and the “Information Literacy” in 2000–2018, with the highest frequency, were identified as the most widely used keywords of KO in the world. In the first period, the cluster “Knowledge Management” had the highest centrality, the cluster “Strategic Planning” had the highest density in 2000–2018 and the cluster “Information Retrieval” had the highest centrality and density. The two-dimensional map of KO’s thematic and clustering of KO topics by cluster analysis method indicates that in the periods examined in this study, thematic clusters had much overlap in terms of concept and content.

The present article uses a longitudinal study to examine the KO’s publications in the past half-century. This paper also uses hierarchical clustering and multidimensional scaling methods. Studying the concepts and thematic trends in KO can impact organizing information as the core of libraries, museums and archives. Also, it can scheme information organizing and promote knowledge management. Because the results obtained from this article can help KO policymakers determine and design the roadmap, research planning, and micro and macro budgeting processes.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.

Archives and records are important resources for individuals, organizations and the country. The academic archives are created and maintained for the effective execution of university educational and corporate functions. The archives management practices in universities are being studied in the developed countries; however, a scarcity of empirical research is observed in the context of developing countries, for instance, Pakistan. Thus, the purpose of this study is to assess the archives management practices performed in the archival units of University of the Punjab (UoP), Lahore, in association with the successful execution of university educational functions.

A structured survey questionnaire was developed to collect responses from the record-keepers and archives monitoring staff using a complete enumerative (census) approach. The collected data were analyzed in SPSS 23.0 in addition to structural equation modeling (SEM) run in AMOS 22v.

The findings of this study revealed an inconsistency regarding the policies and procedures, arrangement and filing records and access and retrieval due to the practice of self-developed procedures in the UoP archival units. Although archives management practices have significant impact on university academic as well as research-related functions, however, there is lack of centralized and standardized practices for archiving records in the UoP. Lack of professional/trained staff and policy document are key limitations in building systematic and standardized archives management system in academic intuitions, particularly in the UoP.

To the best of the authors’ knowledge, this is the first empirical study in Pakistan that has explored archives management practices used in university archives. It also contributes theoretically and methodologically through the underpinnings of archival principles in association with university functions and developing a validated scale to explore archives management practices in universities. The findings of this study may be helpful for the concerned bodies, university administrations and archives managers to establish, manage and improve the academic archives systematically.

The purpose of this paper is to provide a new and useful formulation of relevance.

This paper is formulated as a conceptual argument. It makes the case for the utility of considering relevance to be function of use in creative processes.

There are several corollaries to formulating relevance as a function of use. These include the idea that objects by themselves cannot be relevant since use assumes interaction; the affordances of objects and how they are perceived can affect what becomes relevant but are not in themselves relevant; relevance is not an essential characteristic of objects; relevance is transient; potential relevance (what might be relevant in the future) can be distinguished from what is relevant in use and from what has been relevant in the past.

The paper shows that its new formulation of relevance brings improved conceptual and terminological clarity to the discourse about relevance in information science. It demonstrates that how relevance is articulated conceptually is important as its conceptualization can affect the ways that users are able to make use of information systems and, by extension, how information systems can facilitate or disable the co-production of creative outcomes. The paper also usefully expands investigative opportunities by suggesting relevance and creativity are interrelated.

The present study aimed to investigate the impact of social networks on the use of academic libraries by university students.

The method used in the present study was a survey. The statistical population included 461 university students. The data collection tool was a questionnaire. The result of the Cronbach test was equal to 0.726 indicating the acceptable reliability of the questionnaire. For data analysis, descriptive statistical methods and inferential statistical methods using SPSS 21 software were employed.

The findings showed that 243 of the participants used social networks for 4–6 h a day, 192 students never used university libraries and 229 used the university library only once in a month. Communication with friends was also reported to be one of the main goals in using social networks. The results of regression analysis also indicated that four predictor variables including information retrieval, social influence, trust and attractiveness of social networking environment were statistically able to explain the variance of reluctance to use university libraries.

The present study is one of the few studies that has examined the negative impact of social networks on visiting university libraries.

In federated search, a query is sent simultaneously to multiple resources and each one of them returns a list of results. These lists are merged into a single list using the results merging process. In this work, the authors apply machine learning methods for results merging in federated patent search. Even though several methods for results merging have been developed, none of them were tested on patent data nor considered several machine learning models. Thus, the authors experiment with state-of-the-art methods using patent data and they propose two new methods for results merging that use machine learning models.

The methods are based on a centralized index containing samples of documents from all the remote resources, and they implement machine learning models to estimate comparable scores for the documents retrieved by different resources. The authors examine the new methods in cooperative and uncooperative settings where document scores from the remote search engines are available and not, respectively. In uncooperative environments, they propose two methods for assigning document scores.

The effectiveness of the new results merging methods was measured against state-of-the-art models and found to be superior to them in many cases with significant improvements. The random forest model achieves the best results in comparison to all other models and presents new insights for the results merging problem.

In this article the authors prove that machine learning models can substitute other standard methods and models that used for results merging for many years. Our methods outperformed state-of-the-art estimation methods for results merging, and they proved that they are more effective for federated patent search.

The study examined the personal information management (PIM) challenges encountered by faculty in six universities in Ghana, their information refinding experiences and the perceived role of memory. The study tested the hypothesis that faculty PIM performance will significantly differ when the differences in the influence of personal factors (age, gender and rank) on their memory are considered.

The study was guided by a sample survey design. A questionnaire designed based on themes extracted from earlier interviews was used to collect quantitative data from 235 faculty members from six universities in Ghana. Data analysis was undertaken with a discrete multivariate Generalized Linear Model to investigate how memory intermediates in the relationship between age, gender and rank, and, refinding of stored information.

The paper identified two subfunctions of refinding (Refinding 1 and Refinding 2) associated with self-confidence in information re-finding, and, memory (Memory 1 and Memory 2), associated with the use of complimentary frames to locate previously found and stored information. There were no significant multivariate effects for gender as a stand-alone variable. Males who were aged less than 39 could refind stored information irrespective of the memory class. Older faculty aged 40–49 who possess Memory 1 and senior lecturers who possess Memory 2 performed well in refinding information. There was a statistically significant effect of age and memory; and rank and memory.

This study was limited to faculty in Ghana, whereas the study itself has implications for demographic differences in PIM.

Identifying how memory mediates the role of personal factors in faculty refinding of stored information will be necessary for the efforts to understand and design systems and technologies for enhancing faculty capacity to find/refind stored information.

Understanding how human memory can be augmented by technology is a great PIM strategy, but understanding how human memory and personal factors interplay to affect PIM is more important.

PIM of faculty has been extensively examined in the literature, and limitations of memory has always been identified as a constraint. Human memory has been augmented with technology, although the outcome has been very minimal. This study shows that in addition to technology augmentation, personal factors interplay with human memory to affect PIM. Discrete multivariate Generalized Linear Model applied in this study is an innovative way of addressing the challenges of assimilating statistical methodologies in psychosocial disciplines.