Search results

This study aims to examine Big Data and the management of libraries in the era of the Fourth Industrial Revolution and its implications for policymakers in Nigeria.

A qualitative methodology was used, involving the administration of open-ended questionnaires to librarians from six selected federal universities located in Southwest Nigeria.

The findings of this research highlight that a significant proportion of librarians are well-acquainted with the relevance of big data and its potential to positively revolutionize library services. Librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study exclusively focuses on the Nigerian context, overlooking insights from other African countries. As a result, it may not be possible to generalize the study’s findings to the broader African library community.

To the best of the authors’ knowledge, this study is unique because the paper reported that librarians generally express favorable opinions concerning the relevance of big data, acknowledging its capacity to enhance decision-making, optimize services and deliver personalized user experiences.

This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

Over the last two decades, there has been a significant increase in incivility within the higher education sector, potentially due to mounting pressure and demands on academics, both collectively and individually. The effects on various aspects of academia, such as knowledge and learning, however, remain largely unexplored. The purpose of this research is to fill the gap by performing a theoretical trend analysis and subsequently empirically investigating the impact of workplace incivility on research scholars’ learning engagement and knowledge sharing intentions, including the mediating role of self-esteem.

This study uses a three-stage methodological process: first, a thorough theoretical (bibliographic) analysis of scientific publications, using Biblioshiny, to identify the trends of workplace incivility; second, an empirical, qualitative exploration of the emergent themes and subthemes based on 102 in-depth interviews with research scholars, using NVivo 12 Plus; and third, quantitative testing, using 154 responses and structural equation modeling.

The authors verify a visible negative association between incivility and learning engagement, incivility and knowledge sharing intentions as well as self-esteem’s mediating effect on this relationship. Also, the thematic analysis revealed three distinct themes: the type of incivility; reasons for such incidences; and the impact of such incidences on research scholars.

The research bears implications both to theory and practice. Regarding the former, the gravity and graveness of incivility versus knowledge and learning, within the academic workplace environment, are not simply highlighted, but analyzed and refined, with explicit findings of both scholarly and practicable worth; that also provide solid foundations and avenues for future research.

Further to its primary findings, the research contributes to extant knowledge by elucidating and explicating the topic, both theoretically and empirically, as well as by presenting implications for theory and practice. Regarding practical implications, this research sheds light on how to develop an appropriate organizational culture that facilitates learning engagement and increases knowledge sharing intentions, by nurturing the identified explicit and underlying motivators of civility.

There have been numerous publications on human monkeypox since it was reported. With the help of bibliometric analysis, this study examined research hotspots and future trends related to human monkeypox. Science mapping was used in this study to identify influential monkeypox researchers, institutions, articles, keywords, thematic structures, and clusters of articles.

Based on a validated search query, bibliometric analysis of data collected from Web of Science from 1989 to September 2022 was conducted. Using the “Title-Keyword-Abstract” search option, the search query consisted of keywords “Monkeypox” OR “Monkeypox virus” OR “monkeypox” OR “monkey pox” OR “MPXV.” With the state-of-the-art tools Bibliometrix package of R Studio and VOSviewer, performance analysis and science mapping, as a part of standard bibliometric research of monkeypox research were conducted.

Researchers published 708 monkeypox papers from 1989 to September 2022, with American researchers publishing 460 papers. Further, USA had the highest international cooperation in terms of collaborative research output. Centers for Disease Control and Prevention (CDC) is a global leader in monkeypox research since it is the most prolific and collaborative organization. There have been the most published papers on monkeypox in the Journal of Virology. Damon Inger K is also the most prolific and influential researcher in monkeypox research, with the highest number of publications and citations. In total, 1,679 keywords were identified in the study. From the cluster analysis four themes were identified in monkeypox research. They are (1) clinical features, (2) monkeypox virus epidemiology, (3) monkeypox virus vaccine defense, and (4) monkeypox virus-related treatment measures.

Analysis of collaboration, findings, networks of research, and visualization separates this study from traditional metrics analysis. Currently, there are no similar studies with similar objectives based on the authors' knowledge.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

The topic of artificial intelligence (AI) has been expanding rapidly in recent years, gaining the attention of academics and practitioners. This study provides a structured literature review (SLR) on AI and management decisions (MDs) by analysing the scientific output and defining new research topics.

The study uses a rigorous methodological approach to summarise the state of the art of the past literature. The authors used Scopus as the database for data collection and utilised the Bibliometrix R package. In total, 204 peer-reviewed English articles were collected and analysed.

The results showed that literature in this field is emerging. Studies are focused on using AI as forecasting and classification for management decision-making, AI as a tool to improve knowledge management in organisations and extract information. The cluster analysis revealed the presence of five thematic clusters of studies on the topic.

The study’s originality lies in providing a new perspective on AI for MDs. In particular, the analysis reveals a new classification of research streams and provides fruitful research questions to continue research on the topic.

This paper aims to check the fragmentation of knowledge across multiple sources of evidence, identifying, scrutinizing and outlining suggestions concerning the challenges researchers face when using multiple sources of data to identify studies.

This study produced a comprehensive database of 15,848 items from Scopus, Web of Science and EBSCO on the organizational growth and decline topics. The analyses carried out to check the fragmentation of scientific knowledge and the challenges in identifying studies have made use of the basic data frame functions in R’s language and the Bibliometrix and Corpus R’s packages.

This study confirms the fragmentation of scientific knowledge as well as it identifies the following challenges: missing information in key fields, nonexistence of standards in terminology, limitations on data extraction, duplicates and multiple formats of cited reference. Additionally, it suggests practical coping procedures and advances implications for stakeholders and an agenda for future research.

This study provides valuable and practical examples with empirical confirmation of scientific knowledge fragmentation and offers an integrated view of many challenges in the process of identifying studies. Moreover, by offering suggestions to address these challenges, this study not only offers a practical guide to scientific researchers but also initiates a wider discussion regarding knowledge organizing in social sciences.

This paper aims to offer a new history of management by tracing a religious dimension of scientific management. The thesis is that the good was foundational for bringing scientific management to success in Taylor’s native Quaker Philadelphia in the 1880s. The paper’s main contribution is to contrast the philosophical origins of Taylor’s ideas in scientific management to his native Quaker roots, and how Taylor, over time, into the 1910s, wrestled with this issue.

The paper is situated in historical interpretivism and subjectivism, leaning on contextual and narrative research on religious morality.

Quaker morality prevented managerial opportunism at Taylor’s Midvale Steel in the 1880s. Conversely, by the 1900s and 1910s, interest conflicts between workers and managers escalated when scientific management moved out of its traditional cultural contexts of Quaker Philadelphia and spread across the USA. The historical implication is, already for Taylor’s time, that scientific management never was the “one-best way” of management.

Future research needs to deepen and broaden research on scientific management when tracing the significance of religion and culture in management thought.

The paper has implications for modern studies of business morality by uncovering the practical relevance of religious business ethics at the outset of management studies.

The historic emergence of scientific management points to a theory of institutional evolution and economic growth, when religiously grounded governance of the firm deinstitutionalized, and institutional economic governance, with different but superior economic advantages, progressed by the 1900s.

The paper suggests an alternative version of the intellectual heritage of management studies by tracing the legacy of Taylor’s Quakerism and how religious and cultural ideas contributed to the formation of science in management.

The paper aims to provide an overview of the state-of-the-art of the event industry in the context of digitalization to understand how digital technologies change the event industry and what research topics are the most promising for further exploration.

A bibliometric analysis of the existing body of knowledge on the topic was conducted and the results were visualized using CiteSpace 5.8.R3. A total of 1999 articles and proceeding papers from the Web of Science Core Collection published between 2007 and 2022 were selected for our analysis. Based on the articles and proceeding papers in the Web of Science Core Collection database, we selected a set of publications for our analysis. The data were obtained through specific keywords related to our research topic. The method involves a process of three main stages: data collection, data processing and the bibliometric analysis.

Co-citation analysis indicated that issues of crowd management and tracking human mobility during mass events are important for the event industry and that technologies such as the Internet of Things, special-purpose mobile applications and systems make it easier for an event organizer to handle the issues. The findings demonstrated a weak scientific collaboration between countries in the topic studied and shift of research hotspots to study of satisfaction, motivation and behavioral patterns of events attendees. Based on this analysis, three directions for future research were revealed.

The results should be interpreted in light of our sample, because the analysis was conducted within our sample which has boundaries. We collected data from all categories in the Web of Science Core Collection database, but we considered only articles and proceeding papers as opposed to all possible types of scientific publications and other databases. In the study, we focused on detecting the state-of-the-art of the event industry in the context of digitalization overall. More specific topics that could be analyzed remain, for example, the dependency of digital technologies from the event type, etc.

This study reflects the state-of-the-art of the event industry in the context of digitalization. It provides researchers with key developmental trends in the event industry, which assists them in more deeply understanding the evolution of research hotspots in the field during last 15 years and defining future research agenda. The paper presents an overview of digital technologies used in various types of events and describes the issues and results related to the implementing digital technologies. The results obtained were extremely important, as they can be used by event managers and organizers to enhance customers’ experience during the events.

This study reflects the state-of-the-art of the event industry in the context of digitalization. This is the first attempt to make an overall analysis of scientific papers published in the Web of Science Core Collection on the topic studied without excluding any categories. The search procedure is transparent, and the results can be reproduced in other search fields using the same approach. Based on this analysis, three directions for future research were revealed including technological aspects of online event-based social networks, issues of crowd management and security at mass events and issues of attendees’ acceptance of novel digital technologies.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.