Review on emerging research topics with key-route main path analysis

An active learning-based approach for screening scholarly articles about the origins of SARS-CoV-2

To build a full picture of previous studies on the origins of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), this paper exploits an active learning-based approach to screen scholarly articles about the origins of SARS-CoV-2 from many scientific publications. In more detail, six seed articles were utilized to manually curate 170 relevant articles and 300 nonrelevant articles. Then, an active learning-based approach with three query strategies and three base classifiers is trained to screen the articles about the origins of SARS-CoV-2. Extensive experimental results show that our active learning-based approach outperforms traditional counterparts, and the uncertain sampling query strategy performs best among the three strategies. By manually checking the top 1,000 articles of each base classifier, we ultimately screened 715 unique scholarly articles to create a publicly available peer-reviewed literature corpus, COVID-Origin. This indicates that our approach for screening articles about the origins of SARS-CoV-2 is feasible.

Cover papers of top journals are reliable source for emerging topics detection: a machine learning based prediction framework

The detection of emerging trends is of great interest to many stakeholders such as government and industry. Previous research focused on the machine learning, network analysis and time series analysis based on the bibliometrics data and made a promising progress. However, these approaches inevitably have time delay problems. For the reason that leader papers of “emerging topics” share the similar characters with the “cover papers”, this study present a novel approach to translate the “emerging topics” detection to “cover paper” prediction. By using “AdaBoost model” and topic model, we construct a machine learning framework to imitate the top journal (chief) editor’s judgement to select cover paper from material science. The results of our prediction were validated by consulting with field experts. This approach was also suitable for the Nature, Science, and Cell journals.

Breakthrough potential of emerging research topics based on citation diffusion features

This article uses the characteristics of citation curves in emerging research topics (ERTs) and combines them with the ERTs’ knowledge bases to draw conclusions by comparing their development patterns. The goal of this study is to enrich the toolset for predicting breakthroughs in scientific research. A set of multidimensional and practical bibliometric indicators is used to identify ERTs, to further identify the knowledge bases of ERTs and construct citation curves for both ERTs and their knowledge bases. The development trends of the citation curves of ERTs and their knowledge bases in different time periods are compared and analysed from two dimensions: knowledge transition and continuous growth. We use the field of stem cell research to test our method. Based on the outcome of the analysis, we can assess the breakthrough potential of ERTs. The stratification, transition and recent changes of the citation curve can be used as a basis for analysing and assessing the ERTs’ breakthrough potential. The combination of different citation diffusion patterns of ERTs and their knowledge bases can improve the effectiveness of identifying ERTs that can become breakthrough innovations.

A novel developmental trajectory discovery approach by integrating main path analysis and intermediacy

As a widely used technique for discovering developmental trajectory of a specific field of science and technology, main path analysis armed with global search strategy prefers longer citation paths rather than shorter ones. An obvious feature of longer main paths is that the theme of documents may not be so coherent, though longer paths may provide more details on the development of a field than shorter ones. Thereupon, a new measure, named as intermediacy, was proposed in the literature for recognising important scientific publications. However, the intermediacy is only applicable to the citation network with one single target node and one single source node. For purpose of loosening this limitation of the intermediacy and benefitting from main path analysis and intermediacy, this work raises an alternative approach for discovering developmental trajectory by combining node importance and edge importance via edge and node integrated modes. Extensive experimental results on the weak signals and education fields indicate that similar trajectories can be obtained through these two integrated modes, and richer implications can be encoded in our discovered trajectories than those from main path analysis and intermediacy. In addition, our framework is able to scale very well to a large citation network.

Tracing knowledge evolution flows in scholarly restaurant research: a main path analysis

Restaurant research has received significant attention globally. This article aims to examine the evolution and the knowledge structure of restaurant research over the past decades. We also investigate the restaurant research hotspots and knowledge diffusion paths based on 1489 articles extracted from the Web of Science database. Furthermore, we conduct a keyword co-occurrence network analysis and four different types of main path analyses to scrutinize the historical formation of the restaurant research. Results revealed that restaurant research mainly focused on five research themes: consumer behavior, consumer satisfaction, social media, green restaurants, and authenticity. While consumer behavior has been the mainstream topic, the focus of this line of research has recently shifted from traditional to luxury and ethnic restaurants. Furthermore, our analysis has detected several recent changes in response to the COVID-19 pandemic. By examining the knowledge structure of restaurant research, we reveal its knowledge diffusion paths and provide avenues for future research in this vast and interdisciplinary research field.

Knowledge Diffusion of the Internet of Things (IoT): A Main Path Analysis

The Internet of Things (IoT) is a concept that has attracted significant attention since the emergence of wireless technology. The knowledge diffusion of IoT takes place when an individual disseminates his knowledge of IoT to the persons to whom he is directly connected, and knowledge creation arises when the persons receive new knowledge of IoT, which is combined with their existing knowledge. In the current literature, several efforts have been devoted to summarising previous studies on IoT. However, the rapid development of IoT research necessitates examining the knowledge diffusion routes in the IoT domain by applying the main path analysis (MPA). It is crucial to update prior IoT studies and revisit the knowledge evolution and future research directions in this domain. Therefore, this paper adopts the keyword co-occurrence network and MPA to identify the research hotspots and study the historical development of the IoT domain based on 27,425 papers collected from the Web of Science from 1970 to 2020. The results show that IoT research is focused on IoT applications for smart cities, wireless networks, blockchain technology, computing technologies, and AI technologies. The findings from the MPA address the need to explore the knowledge evolution in the IoT domain. They also provide a valuable guide to disseminate the knowledge of IoT among researchers and practitioners, assisting them to understand the history, present and future trends of IoT development and implementation.

Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis

Over the two last decades, coronaviruses have affected human life in different ways, especially in terms of health and economy. Due to the profound effects of novel coronaviruses, growing tides of research are emerging in various research fields. This paper employs a co-word analysis approach to map the intellectual structure of the coronavirus literature for a better understanding of how coronavirus research and the disease itself have developed during the target timeframe. A strategic diagram has been drawn to depict the coronavirus domain's structure and development. A detailed picture of coronavirus literature has been extracted from a huge number of papers to provide a quick overview of the coronavirus literature. The main themes of past coronavirus-related publications are (a) "Antibody-Virus Interactions," (b) "Emerging Infectious Diseases," (c) "Protein Structure-based Drug Design and Antiviral Drug Discovery," (d) "Coronavirus Detection Methods," (e) "Viral Pathogenesis and Immunity," and (f) "Animal Coronaviruses." The emerging infectious diseases are mostly related to fatal diseases (such as Middle East respiratory syndrome, severe acute respiratory syndrome, and COVID-19) and animal coronaviruses (including porcine, turkey, feline, canine, equine, and bovine coronaviruses and infectious bronchitis virus), which are capable of placing animal-dependent industries such as the swine and poultry industries under strong economic pressure. Although considerable research into coronavirus has been done, this unique field has not yet matured sufficiently. Therefore, "Antibody-virus Interactions," "Emerging Infectious Diseases," and "Coronavirus Detection Methods" hold interesting, promising research gaps to be both explored and filled in the future.

A systematic literature review of RFID in supply chain management

Purpose

The findings of this paper throw light on the focal research areas within RFID in the supply chain, which serves as an effective guideline for future research in this area. This research, therefore, contributes to filling the gap by carrying out an SLR of contemporary research studies in the area of RFID applications in supply chains. To date, SLR augmented with BA has not been used to study the developments in RFID applications in supply chains.

Design/methodology/approach

We analyze 556 articles from years 2001 to date using Systematic Literature Review (SLR). Contemporary bibliometric analysis (BA) tools are utilized. First, an exploratory analysis is carried, out revealing influential authors, sources, regions, among other key aspects. Second, a co-citation work analysis is utilized to understand the conceptual structure of the literature, followed by a dynamic co-citation network to reveal the evolution of the field. This is followed by a multivariate analysis is performed on top-100 cited papers, and k-means clustering is carried out to find optimal groups and identify research themes. The influential themes are then pointed out using factor analysis.

Findings

An exploratory analysis is carried out using BA tools to provide insights into factors such as influential authors, production countries, top-cited papers and frequent keywords. Visualization of bibliographical data using co-citation network analysis and keyword co-occurrence analysis assisted in understanding the groups (communities) of research themes. We employed k-means clustering and factor analysis methods to further develop these insights. A historiographical direct citation analysis also unveils potential research directions. We observe that RFID applications in the supply chain are likely to benefit from the Internet of Things and blockchain Technology along with the other machine learning and visualization approaches.

Originality/value

Although several researchers have researched RFID literature in relation to supply chains, these reviews are often conducted in the traditional manner where the author(s) select paper based on their area of expertise, interest and experience. Limitation of such reviews includes authors’ selection bias of studies to be included and limited or no use of advanced BA tools for analysis. This study fills this research gap by conducting an SLR of RFID in supply chains to identify important research trends in this field through the use of advanced BA tools.

Exploring the knowledge development trajectories of the supply chain finance domain: a main path analysis

Purpose

Supply chain finance (SCF), which is able to manage financial flows along the supply chains effectively, has received wide attention from all over the world. Faced with the increasing number of outputs, the purpose of this paper is to investigate the SCF development over the past decades effectively, including the hot topics, knowledge diffusion trajectories and structure.

Design/methodology/approach

This paper adopts the keyword co-occurrence cluster and main path analysis (MPA) including four types of main paths, studying the historical development of SCF based on 2,233 papers retrieved from Web of Science during 1970–2019.

Findings

The results show that: (1) the research focuses on several aspects, including trade credit, supply chain management, procurement, health financing and sustainability, etc. and (2) trade credit financing has been the mainstream and the research focus has shifted from one-level trade credit to two-level trade credit. Recently, there is a trend to use game-theoretic models to find the best solutions for members in the supply chain.

Originality/value

This paper addresses the need to investigate the knowledge evolution in the SCF domain. It provides a framework to study the knowledge diffusion trajectories and structure, which helps scholars to handle thousands of papers effectively and deepen their understanding of the history, present and future trends of SCF development.