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Although universities have extensive research and initiatives in place that align with the United Nations’ Sustainable Development Goals (SDGs), there is still a significant gap in documenting and assessing these efforts. This paper aims to discuss how academic libraries can apply their information management skills and open-access platforms, to facilitate the discoverability and retrieval of evidence on SDGs.

Introduced by a brief literature review on the role of libraries in contributing to the SDGs in general, the authors draw on their personal experiences as metadata specialists, participating in a project aimed at linking their university’s research output to the SDGs. A case study, from the University of Pretoria’s Veterinary Science Library, is used as an example to demonstrate the benefits of resourceful metadata in organising, communicating and raising awareness about the SDGs in the field of veterinary science.

Through practical examples and recommended workflows, this paper illustrates that metadata specialists are perfectly positioned to apply their information management skills and library platforms to facilitate the discoverability and retrieval of evidence on SDGs.

Although there are increasing reports on the contributions of libraries to support the successful implementation of the SDGs, limited information exists on the role of metadata specialists, as well as those with a practical focus.

In this study, STEM resources reviewed in Goodreads were investigated to determine their authorship, linguistic characteristics and impact. The analysis reveals gender disparity favoring titles with male authors.

This paper applies theoretical concepts of knowledge commons to understand how individuals leverage the affordances of the Goodreads platform to share their perceptions of STEM-related books.

The analysis reveals gender disparity favoring titles with male authors. Female-authored STEM publications represent popular science nonfiction and juvenile genres. Analysis of the scholarly impact of the reviewed titles revealed that Google Scholar provides broader and more diverse coverage than Web of Science. Linguistic analysis of the reviews revealed the relatively low aesthetic disposition of reviewers with an emphasis on embodied experiences that emerged from the reading.

This study contributes to the understanding of the impact of popular STEM resources as well as the influence of the language of user-generated reviews on production, consumption and discoverability of STEM titles.

This study aims to investigate the facilitating conditions (FCs) and how these FC affect institutional repository (IR) sustainability practices in public universities in Nigeria.

A survey research design was adopted in this study. The study population comprised 542 librarians from public universities that have IRs across Nigeria. A sample size of 230 librarians was determined using Taro Yamane’s formula. A multi-stage sampling technique was used to select the respondents in three stages, which were purposive, stratified and purposive sampling. A structured, validated questionnaire was used for data collection. Data were analyzed using descriptive and inferential (simple and multiple regression) statistics at a 5% level of significance.

The result revealed that the availability of FCs (ßeta = 0.459, t(211) = 7.719, p = 0.000) has a positive and significant influence on IR sustainability in public university libraries in Nigeria. The F-test (1, 223) value of 59.582 shows that there is sufficient evidence to substantiate the model’s usefulness in explaining IR sustainability. The R² (0.211) indicates that 21.1% of the variation in IR sustainability is explained by the availability of FCs in public university libraries in Nigeria. The finding suggests that the availability of FCs is a vital predictor of IR sustainability in public university libraries in Nigeria. The result also depicts that out of the eight parameters that measure the availability of FCs, it was current awareness of IR that had a positive and significant influence on IR sustainability.

This study concluded that ICT skills and FCs are contributory factors to IR sustainability practices by librarians in public university libraries in Nigeria. It was recommended that university administrators formulate policies that promote the sustainability of IR and provide adequate funds to support IR sustainability. Furthermore, the library management in public university libraries in Nigeria should drive content recruitment and create awareness of the IRs among students and faculty to ensure continued use.

This study aimed to develop a taxonomy of research areas in open government data (OGD) through a bibliometric mapping tool and a qualitative analysis software.

In this study, the authors extracted metadata of 442 documents from a bibliographic database. The authors used a bibliometric mapping tool for familiarization with the literature. After that, the authors used qualitative analysis software to develop taxonomy.

This paper developed taxonomy of OGD with three research areas: implementation and management, architecture, users and utilization. These research areas are further analyzed into seven topics and twenty-eight subtopics. The present study extends Charalabidis et al. (2016) taxonomy by adding two research topics, namely the adoption factors and barriers of OGD implementations and OGD ecosystems. Also, the authors include artificial intelligence in the taxonomy as an emerging research interest in the literature. The authors suggest four directions for future research: indigenous knowledge in open data, open data at local governments, development of OGD-specific theories and user studies in certain research themes.

Early career researchers and doctoral students can use the taxonomy to familiarize themselves with the literature. Also, established researchers can use the proposed taxonomy to inform future research. Taxonomy-building procedures in this study are applicable to other fields.

This study developed a novel taxonomy of research areas in OGD. Taxonomy building is significant because there is insufficient taxonomy of research areas in this discipline. Also, conceptual knowledge through taxonomy creation is a basis for theorizing and theory-building for future studies.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

This study aims to identify and explore different risk typologies associated with consumer acceptance of purchase recommendations from voice assistants (VAs).

Drawing on components of perceived risk, consumer trust theory, and consumption value theory, a research model was proposed and tested using structural equation modeling (SEM) with data from 482 voice shoppers.

The results reveal that, unlike risks associated with physical harm, privacy breaches, and security threats, a variety of other concerns—including financial, psychological, social, performance-related risks, time loss, and the overall perceived risks—significantly influence consumers' willingness to accept VAs purchase recommendations. The effect is mediated by trust in VA purchase recommendations and their perceived value. Different types of risk affect various consumption values, with functional value being the most influential. The model explains 58.6% of the variance in purchase recommendation acceptance and significantly elucidates the variance in all consumption values.

This study contributes crucial knowledge to understanding consumer decision-making processes as they increasingly leverage AI-powered voice-based dialogue platforms for online purchasing. It emphasizes recognizing diverse risk typologies associated with VA purchase recommendations and their impact on consumer purchase behavior. The findings offer insights for marketing managers seeking to navigate the challenges posed by consumers' perceived risks while leveraging VAs as an integral component of modern shopping environments.

This case studies the growth of OYO Hotels (OYO) to illustrate the operational processes necessary to succeed in the service sector. The case allows for a discussion of employee- and customer-management systems, tech-driven solutions, and profit drivers. The material unfolds OYO's growth and its solution for making economy hotels discoverable and bookable online.

The case raises a series of questions around OYO's business model, its ability to translate across global markets, and growth potential. It has been successfully taught in a second-year MBA class on the management of service operations.

This paper aims to explore the decline of the metaverse and the concurrent growth of online videos, examining their implications for libraries. It analyzes factors contributing to the metaverse decline, such as technical issues and privacy concerns, and explores the emergence and popularity of online video platforms.

This study applied the interpretive content/document analysis of literature harvested from different databases of Scopus, Web of Science and Directory of Open Access Journals. Given the shift in digital consumption patterns and their impact on libraries, the paper systematically reviews the literature to elucidate the changing landscape of digital engagement and its implications for library services and outreach efforts.

The paper identifies technical issues, privacy concerns and a lack of interoperability as key factors contributing to the decline of the metaverse. Conversely, the growth of online videos is driven by the emergence of platforms, increasing accessibility to high-speed internet and a diversification of content offerings. These trends reshape user preferences and behaviors, presenting both challenges and opportunities for libraries.

The decline of the metaverse and the rise of online videos underscore the importance of adaptation and innovation for libraries. To remain relevant in the digital age, libraries must transform their services to meet the demand for on-demand content and multimedia resources. The findings highlight the need for libraries to invest in digital literacy and media literacy programs, embrace emerging technologies and forge strategic partnerships with online video platforms. By adapting to changing trends and user preferences, libraries will continue to fulfill their mission of providing access to information and knowledge in the digital age.

Artificial intelligence (AI) is one of today’s rising technologies. AI is a commonly used technology in library services that have the potential to revolutionise the best offerings in the information age. With AI in libraries, users can explore the world of knowledge like never before with smart recommendations tailored to their needs. Overall, AI can enhance the library experience of both the users and library professionals with innovation and smart decisions. Hence, there is no doubt that AI and libraries have a close relationship; nonetheless, the usage and understanding of AI in library services continue to raise concerns, especially in the developing countries which this paper addresses. The purpose of this research paper is to review the current prospects and challenges of implementing AI in library services in developing countries. The primary objective of the study is to discern the pivotal predicaments and obstacles these nations face while implementing AI-based solutions and to propose pragmatic solutions.

The present study adopts a qualitative approach, using content analysis techniques to glean meaningful insights. An extensive review of the extant literature on the subject was conducted, which was meticulously analysed to furnish the findings of this study. The review is limited to English language sources, and searches were conducted using various online academic databases.

The review reveals that the prospects of implementing AI in library services in developing countries are significant, with potential benefits including improved access to information, increased efficiency and productivity and enhanced user experience. However, the review also identifies several challenges, including the lack of infrastructure and resources, the shortage of skilled personnel, the absence of data privacy regulations, digital divide and the high cost of implementing AI-based solutions.

The review suggests several practical solutions to overcome the challenges faced by developing countries in implementing AI in library services. These include partnerships between libraries and technology firms, investment in infrastructure and resources, training and capacity building for library staff and the development of regulatory frameworks to protect user data.

This research paper provides a comprehensive review of the prospects and challenges of implementing AI in library services in developing countries. The study is original in its focus on the perspectives of developing countries, their problems and obstacles. The study also provides practical recommendations that can be used by library managers, policymakers and technology firms to support the implementation of AI-based solutions in developing countries.

Replication is a primary self-correction device in science. In this paper, we have two aims: to examine how and when the results of replications are used in management and organization research and to use the results of this examination to offer guidelines for improving the self-correction process.

Study 1 analyzes co-citation patterns for 135 original-replication pairs to assess the direct impact of replications, specifically examining how often and when a replication study is co-cited with its original. In Study 2, a similar design is employed to measure the indirect impact of replications by assessing how often and when a meta-analysis that includes a replication of the original study is co-cited with the original study.

Study 1 reveals, among other things, that a huge majority (92%) of sources that cite the original study fail to co-cite a replication study, thus calling into question the impact of replications in our field. Study 2 shows that the indirect impact of replications through meta-analyses is likewise minimal. However, our analyses also show that replications published in the same journal that carried the original study and authored by teams including the authors of the original study are more likely to be co-cited, and that articles in higher-ranking journals are more likely to co-cite replications.

We use our results to formulate recommendations that would streamline the self-correction process in management research at the author-, reviewer- and journal-level. Our recommendations would create incentives to make replication attempts more common, while also increasing the likelihood that these attempts are targeted at the most relevant original studies.