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This study aims to map the scholarly literature on human resource management (HRM) publishing intensity in journals listed in Web of Science (WOS) under the subject category “Information Science and Library Science,” between 1989 and 2022.

The current study used a “bibliometric research design,” which is a quantitative approach. Ten selected bibliometric indicators were used to measure the scientific literature: publication-related metrics, citation-related metrics, citation analysis, co-citation analysis, bibliographic coupling, co-word analysis, co-authorship analysis, network metrics, clustering and visualization. Moreover, Louvain’s clustering algorithm was used for network metrics.

The paper gives empirical insights into the scholarly literature on HRM. The results were analyzed for the 65 sources and 1,412 authors from 60 countries who contributed the most during this period. Moreover, the study highlights a glimpse of funding sources, open-access publishing patterns and venues of publishing.

The study would be very beneficial to researchers and practitioners across disciplines.

This study illustrates that HRM is a multidisciplinary field that is appealing to academics from various disciplines because of its unique emphasis on management, and as such, it necessitates the pooling and integration of people, information, expertise and strategies. The study investigates numerous quantitative indicators such as research trends and collaboration frameworks.

This study aims to measure scientific literature on the emerging research area of “big data” in the field of “library and information science” (LIS).

This study used the “bibliometric method” for data curation. Web of Science and altmetric.com were used. Data analysis and visualisation were done using three widely used powerful data analytics software, R-bibliometrix, VOSviewer and Statistical Package for Social Sciences.

This study revealed the most preferred venues for publication. Furthermore, this study highlighted an association between the Mendeley readers of publications and citations. Furthermore, it was evident that the overall altimetric attention score (AAS) does not influence the citation score of publications. Other fascinating findings were moderate collaboration patterns overall. Furthermore, the study highlighted that big data (BD) research output and scientific influence in the LIS sector are continually increasing.

Findings related to BD analytics in LIS techniques can serve as helpful information for researchers, practitioners and policymakers.

This study contributes to the current knowledge accumulation by its unique manner of blending the two approaches, bibliometrics and altmetrics.

This study aims to examine the impact of intellectual capital on the underwriting risk of insurance companies in Pakistan.

The study used a quantitative research approach and a longitudinal research design, gathering data from 23 insurance companies listed on the Pakistan stock exchange from 2010 to 2022. The value-added intellectual coefficient (VAIC) was used to measure intellectual capital (IC), and unbalanced panel data were analyzed using static and dynamic regression analyses.

The findings demonstrate a significant association between intellectual capital and underwriting risk in insurance companies in Pakistan. Specific components of intellectual capital, such as human capital efficiency (HCE), structural capital efficiency (SCE) and capital employed efficiency (CEE), have a strong negative impact on underwriting risk. Control variables like return on assets, insurer size and leverage also showed significant relationships with underwriting risk.

This research provides new insights into the theoretical understanding of the insurance industry by establishing a direct link between intellectual capital and underwriting risk in the context of Pakistan. It suggests that by improving aspects of intellectual capital, specifically HCE, SCE and CEE, policymakers and managers can reduce underwriting risk.

This work provides an overview of academic articles on the application of artificial intelligence (AI) in healthcare. It delves into the innovation process, encompassing a two-stage trajectory of exploration and development followed by dissemination and adoption. To illuminate the transition from the first to the second stage, we use prospect theory (PT) to offer insights into the effects of risk and uncertainty on individual decision-making, which potentially lead to partially irrational choices. The primary objective is to discern whether clinical decision support systems (CDSSs) can serve as effective means of “cognitive debiasing”, thus countering the perceived risks.

This study presents a comprehensive systematic literature review (SLR) of the adoption of clinical decision support systems (CDSSs) in healthcare. We selected English articles dated 2013–2023 from Scopus, Web of Science and PubMed, found using keywords such as “Artificial Intelligence,” “Healthcare” and “CDSS.” A bibliometric analysis was conducted to evaluate literature productivity and its impact on this topic.

Of 322 articles, 113 met the eligibility criteria. These pointed to a widespread reluctance among physicians to adopt AI systems, primarily due to trust-related issues. Although our systematic literature review underscores the positive effects of AI in healthcare, it barely addresses the associated risks.

This study has certain limitations, including potential concerns regarding generalizability, biases in the literature review and reliance on theoretical frameworks that lack empirical evidence.

The uniqueness of this study lies in its examination of healthcare professionals’ perceptions of the risks associated with implementing AI systems. Moreover, it addresses liability issues involving a range of stakeholders, including algorithm developers, Internet of Things (IoT) manufacturers, communication systems and cybersecurity providers.