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This study aims to enhance the understanding of the current research landscape regarding the utilisation of telepresence robots (TPRs) in education.

The bibliometric and thematic analysis of research publications on TPRs was conducted using papers in the Scopus database up to 2023. The final analysis focused on 53 papers that adhered to the selection criteria. A qualitative analysis was performed on this set of papers.

The analysis found a rising trend in TPR publications, mostly from the USA as conference papers and journal articles. However, these publications lacked technology integration frameworks, acceptance models and specific learning design models. TPRs have proven effective in various learning environments, fostering accessible education, better communication, engagement and social presence. TPRs can bridge geographical gaps, facilitate knowledge sharing and promote collaboration. Obstacles to implementation include technical, physical, social and emotional challenges. Publications were grouped into four thematic categories: didactic methods of using TPRs, TPRs for educational inclusivity, TPR as a teacher mediator and challenges in using TPRs. Despite the significant potential of TPRs, their broader adoption in education is still facing challenges.

This research solely analysed research papers in the Scopus database, limiting TPR publications with the keywords “telepresence robots”, “learning”, “teaching” and “education”, excluding studies with different other keywords.

This study enhances understanding of TPR research in education, highlighting its pedagogical implications. It identifies a gap in the inclusion of technology integration frameworks, acceptance models and learning design models, indicating a need for further research and development.

This study aims to establish a reliable index to identify interdisciplinary breakthrough innovation effectively. We constructed a new index, the D_Div index, for this purpose.

The D_Div index incorporates the degree of interdisciplinarity in the breakthrough index. To validate the index, a data set combining the publication records and citations of Nobel Prize laureates was divided into experimental and control groups. The validation methods included sensitivity analysis, correlation analysis and effectiveness analysis.

The sensitivity analysis demonstrated the D_Div index’s ability to differentiate interdisciplinary breakthrough papers from various categories of papers. This index not only retains the strengths of the existing index in identifying breakthrough innovation but also captures interdisciplinary characteristics. The correlation analysis revealed a significant correlation (correlation coefficient = 0.555) between the interdisciplinary attributes of scientific research and the occurrence of breakthrough innovation. The effectiveness analysis showed that the D_Div index reached the highest prediction accuracy of 0.8. Furthermore, the D_Div index outperforms the traditional DI index in terms of accuracy when it comes to identifying interdisciplinary breakthrough innovation.

This study proposed a practical and effective index that combines interdisciplinary and disruptive dimensions for detecting interdisciplinary breakthrough innovation. The identification and measurement of interdisciplinary breakthrough innovation play a crucial role in facilitating the integration of multidisciplinary knowledge, thereby accelerating the scientific breakthrough process.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

The study responds to the common concerns of authors, reviewers, and editors on writing and publishing high-quality literature review (LR) studies. First, we evolved the background and decision elements on the five parameters of a quality LR paper: Planning, Operationalizing, Writing, Embedding, and Reflecting (POWER), from the editorials and guiding literature. Statistical procedure and refinement of 256 responses from writers, reviewers, and editors revealed 37 decision elements. Finally, a multicriteria-decision-making approach was applied to the detailed responses from the lead editors of ABDC, Scopus, ABS, and WoS journals, and 31 decision elements were found strong enough to represent these five parameters on the quality of LR studies. All five parameters are found important to be considered. However, a high priority is suggested for embedding (the results coming out of the review) and operationalizing (the process of conducting the review), whereas reflection, writing, and planning of LR papers still remain important. The purpose of the POWER framework is to overcome the challenges and decision dilemmas faced by writers and decision-makers. The POWER framework acts as a guiding tool to conduct LR studies in general and business management scholars in specific ways. In addition, this study provides a checklist (Table 6) and template (Appendix A1) of a quality LR study to its stakeholders.

Systematic literature review (SLR) papers have gained significant importance during the last years as many reputed journals have asked for literature review submissions from the authors. However, at the same time, authors are experiencing a high number of desk rejections because of a lack of quality and its contribution to the existing body of knowledge. Therefore, the purpose of this paper is to offer guidance to researchers who intend to communicate SLR papers in top-rated journals. We attempt to offer a guide to buddy researchers who plan to write SLR papers. This purpose is achieved by clearly stating how the traditional review method is different from SLR, when and how can each type of literature review method be used, writing effective motivation of a review paper and finally how to synthesize the available literature. We have also presented a few suggestions for writing an impactful SLR in the last. Overall, this chapter serves as a guide to various aspirants of SLR paper to understand the prerequisites of an SLR paper and offers deep insights to bring in more clarity before writing an SLR paper, thereby reducing the chances of desk rejection.

The purpose of this paper is to discuss how retracted scientific papers become zombie papers and why they are problematic and to encourage librarians to become active in addressing these problems.

This paper explains what zombie papers are, how they are created and the potential impact they can have on the body of scientific literature. It explains how and why they are different than other common types of misleading scientific publications. It also explores recent developments such as the growth of artificial intelligence (AI) technologies and changes to organizations that make data about paper retractions available.

While journal retractions are as old as scientific publishing itself, the seriousness of retractions persisting and being used in the body of scientific literature has recently been recognized as a serious concern. The rise of new AI technologies such as ChatGPT has made the presence of zombie papers in the data used to train large language models (LLMs) extremely concerning.

While librarians are well-aware of journal retractions and most include information about them in their information literacy training, concerns around zombie papers and their potential presence in the data used to train LLMs will likely be a new consideration for most.

The purpose of this paper is to explore how gamification may be used for project stakeholder engagement.

The paper presents the results of a systematic literature review of extant research concerning the gamification of projects. Based on this, an agenda for future studies is outlined.

Extant research on the gamification of projects is scarce and scattered among various disciplines, but the engineering fields dominate. The research performed does indicate that gamification may be used for involving stakeholders in projects, primarily by promoting learning, but also by engaging them, motivating action and solving problems.

In several cases, extant research display poor quality in research design and a lack in cross-disciplinary perspectives, which means that more research is needed. The users’ perspective is often lacking. Furthermore, the ideas gamification might be “hidden” within other technologies.

The findings of this research may assist project management practitioners in the endeavor of adopting gamification principles to better involve stakeholders.

The study fills a gap in summarizing the research on how gamification may be used to promote project stakeholder engagement. Based on this, it proposes a research agenda for future research on the use of gamification to promote project stakeholder engagement.

This paper presents a review of the state of the art on the application of blockchain and smart contracts to the peer-review process of scientific papers. The paper seeks to analyse how the main characteristics of the existing blockchain solutions in this field to detect opportunities for the improvement of future applications.

A systematic review of the literature on the subject was carried out in three databases recognized by the research community (IEEE Xplore, Scopus and Web of Science) and the Frontiers in Blockchain journal. A total of 1,967 articles were initially found, and after the exclusion process, the 26 remaining articles were classified according to the following dimensions: System Type, Open Access, Review Type, Reviewer Incentive, Token Economy, Blockchain Access, Blockchain Identification, Blockchain Used, Paper Storage, Anonymity and Maturity of the solution.

Results show that the solutions are normally concerned on offering incentives to the reviewers' work (often monetary). Other common general preferences among the solutions are the adoption of open reviews, the use of Ethereum, the implementation of publishing ecosystems and the use of InterPlanetary File System to the storage of the papers.

There are currently no studies covering the main aspects of blockchain solutions in the field of scientific peer review. The present study provides an overall review of the topic, summarizing important information on the current research and helping new adopters to develop solutions grounded on the existing literature.

The purpose of this paper is to structure existing research on knowledge management (KM) in small- and medium-sized enterprises (SMEs) to offer a comprehensive overview of research strands and topics in KM in SMEs to determine their evolution over time.

The paper, which is considered a follow-up literature review, is based on a systematic literature review that covers 180 scientific papers that were published since the review paper by Durst and Edvardsson in 2012 that covered 36 papers.

The findings of this review and those of the aforementioned review are brought together in the form of an overview that structures research on KM in SMEs based on themes that, in turn, allow the derivation of promising research directions and research questions aimed at structuring future research on KM in SMEs.

By combining the findings of this review with the findings from the review published in this journal in 2012, this paper offers, to the best of the authors’ knowledge, the most comprehensive literature review on KM in SMEs produced to date.

Business case (BC) analyses are performed in many different business fields, to create a report on the feasibility and competitive advantage of an intervention within an existing organisation to secure commitment from management to invest. However, most BC research papers on decisions regarding internal funding are either based on anecdotal insights, on analyses of standards from practice, or focused on very specific BC calculations for a certain project, investment or field. A clear BC process method is missing.

This paper aims to describe the results of a systematic literature review of 52 BC papers that report on further conceptualisation of what a BC process should behold.

Synthesis of the findings has led to a BC definition and composition of a 20 step BC process method. In addition, 29 relevant theories are identified to tackle the main challenges of BC analyses in future studies to make them more effective. This supports further theoretical development of academic BC research and provides a tool for BC processes in practice.

Although there is substantial scientific research on BCs, there was not much theoretical development nor a general stepwise method to perform the most optimal BC analysis.