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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 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.

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.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

The purpose of this study is to increase awareness of current supply chain (SC) security-related issues by providing an extensive analysis of existing SC security solutions and their limitations. The security of SCs has received increasing attention from researchers, due to the emerging risks associated with their distributed nature. The increase in risk in SCs comes from threats that are inherently similar regardless of the type of SC, thus, requiring similar defence mechanisms. Being able to identify the types of threats will help developers to build effective defences.

In this work, we provide an analysis of the threats, possible attacks and traceability solutions for SCs, and highlight outstanding problems. Through a comprehensive literature review (2015–2021), we analysed various SC security solutions, focussing on tracking solutions. In particular, we focus on three types of SCs: digital, food and pharmaceutical that are considered prime targets for cyberattacks. We introduce a systematic categorization of threats and discuss emerging solutions for prevention and mitigation.

Our study shows that the current traceability solutions for SC systems do not offer a broadened security analysis and fail to provide extensive protection against cyberattacks. Furthermore, global SCs face common challenges, as there are still unresolved issues, especially those related to the increasing SC complexity and interconnectivity, where cyberattacks are spread across suppliers.

This is the first time that a systematic categorization of general threats for SC is made based on an existing threat model for hardware SC.

The purpose of this study is to investigate the current research data management practices among researchers in Ghana and their impact on data reuse and collaborative research. The study aims to identify the methods used by researchers to store and preserve their research data, as well as to determine the extent to which researchers share their data with others.

The study uses a mixed-method research strategy to blend qualitative and quantitative data and is conducted at two public and two private universities in Ghana.

The study revealed that researchers in Ghana currently store and preserve their research data using personal devices, such as laptops, CDs and external flash drives, rather than keeping the data in university data repositories. They also do not share their research data with others, which negatively affects collaborative research. The current practice of storing data on personal devices and not sharing data with others hinders collaborative research. The study recommends that universities in Ghana revise their research policy documents to address RDM-related issues such as data storage, data preservation, data sharing and data reuse.

The study was conducted at two public and two private universities in Ghana, but the findings were placed in a wider context through appropriate references.

This study emphasises the need for sound research data management procedures to support research collaboration and data reuse in Ghana. Universities should provide incentives to academics to disclose their data to encourage data sharing and collaboration.

The government and management of universities should consciously invest in the needed technologies and equipment to implement research data management in their universities.

This study looks at how researchers in Ghana manage their research data and how it affects data reuse and collaborative research.