Search results

Supply chain risk management (SCRM) is a multi-stage process that handles the adverse impact of disruptions in the supply chain network (SCN), and various SCRM techniques have been widely developed in the literature. As artificial intelligence (AI) techniques advance, they are increasingly applied in SCRM to enhance risk management’s capabilities.

In the current, systematic literature review (SLR), which is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, we analysed the existing literature on AI-based SCRM methods without any time limit to categorise the papers’ focus in four stages of the SCRM (identification, assessment, mitigation and monitoring). Three research questions (RQs) consider different aspects of an SCRM method: interconnectivity, external events exposure and explainability.

For the PRISMA process, 715 journal and conference papers were first found from Scopus and Web of Science (WoS); then, by automatic filtering and screening of the found papers, 72 papers were shortlisted and read thoroughly, our review revealed research gaps, leading to five key recommendations for future studies: (1) Attention to considering the ripple effect of risks, (2) developing methods to explain the AI-based models, (3) capturing the external events impact on the SCN, (4) considering all stages of SCRM holistically and (5) designing user-friendly dashboards.

The current SLR found research gaps in AI-based SCRM and proposed directions for future studies.

The aim of this paper is to provide a comprehensive and detailed review of the state-of-the-art mechanisms of knowledge sharing (KS) in the supply chain (SC) field, as well as directions for future research. Briefly, this paper tries to offer a systematic and methodical review of the KS mechanisms in the SC to provide a comparative summary of the selected articles, to collect and describe the factors that have the influence on KS and SC, to explore some main challenges in this field and to present the guidelines to face the existing challenges and outlining the key areas where the KS mechanisms in SC can be improved.

In the current study, a systematic literature review up to 2018 is presented on the supply chain’s mechanisms of KS. The authors identified 21,907 papers, which are reduced to 25 primary studies through the paper-selection process.

The results showed that the KS in SC helps to increase the success of the organizations, improve employee performance, increase competitive advantage, enhance innovation and improve relationships between supplier and consumer. However, there were some weaknesses, such as staff resistance to share knowledge in the SC because of fear of job loss.

There are several limitations to this study. This study limited the search to Google Scholar. There might be other academic journals where Google does not find their paper and they can offer a more complete picture of the related articles. Finally, non-English publications were omitted from this study. It is possible that the research about the application of KS in SC can also be published in other languages. In addition, more studies need to be carried out using other methodologies such as interviews.

The paper presents a comprehensive structured literature review of the articles’ mechanisms of KS in SC. The paper’s findings can offer insights into future research needs. By providing comparative information and analyzing the current developments in this area, this paper will directly support academics and practicing professionals for better knowing the progress in KS mechanisms.

It is possible to see effective use of Artificial Intelligence-based systems in many fields because it easily outperforms traditional solutions or provides solutions for the problems not previously solved. Prediction applications are a widely used mechanism in research because they allow for forecasting of future states. Logical inference mechanisms in the field of Artificial Intelligence allow for faster and more accurate and powerful computation. Machine Learning, which is a sub-field of Artificial Intelligence, has been used as a tool for creating effective solutions for prediction problems.

In this chapter the authors will focus on employing Machine Learning techniques for predicting data for future states of economic using techniques which include Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, Dynamic Boltzmann Machine, Support Vector Machine, Hidden Markov Model, Bayesian Learning on Gaussian process model, Autoregressive Integrated Moving Average, Autoregressive Model (Poggi, Muselli, Notton, Cristofari, & Louche, 2003), and K-Nearest Neighbor Algorithm. Findings revealed positive results in terms of predicting economic data.

Introduction: The insurance industry has unprecedented growth, and the demand for insurance has outgrown in the recent past due to the prevailing pandemic. The companies have a large base of the data set at their disposal, and companies must appropriately handle these data to come out with valuable solutions. Data mining enables insurance companies to gain an insightful approach to map strategies and gain competitive advantage, thus strengthening the profits that will allow them to identify the effectiveness of back-propagation neural network (BPNN) and support vector machines (SVMs) for the companies considered under study. Data mining techniques are the data-driven extraction techniques of information from large data repositories, thus discovering useful patterns from the voluminous data (Weiss & Indurkya, 1998).

Purpose: The present study is performed to investigate the comparative performance of BPNNs and SVMs for the selected Indian insurance companies.

Methodology: The study is conducted by extracting daily data of Indian insurance companies listed on the CNX 500. The data were then transformed into technical indicators for predictive model building using BPNN and SVMs. The daily data of the selected insurance companies for four years, that is, 1 April 2017 to 21 March 2021, were used for this. The data were further transformed into 90 data sets for different periods by categorising them into biannual, annual, and two-year collective data sets. Additionally, the comparison was made for the models generated with the help of BPNNs and SVMs for the six Indian insurance companies selected under this study.

Findings: The findings of the study exhibited that the predictive performance of the BPNN and SVM models are significantly different from each other for SBI data, General Insurance Corporation of India (GICRE) data, HDFC data, New India Assurance Company Ltd. (NIACL) data, and ICICIPRULI data at a 5% level of significance.

The purpose of this paper is to study the presence of highly cited papers of Nature in social media websites and tools. It also tries to examine the correlation between altmetric and bibliometric indicators.

This descriptive study was carried out using altmetric indicators. The research sample consisted of 1,000 most-cited articles in Nature. In February 2019, the bibliographic information of these articles was extracted from the Scopus database. Then, the titles of all articles were manually searched on Google, and by referring to the article in the journal website and altmetric institution, the data related to social media presence and altmetric score of articles were collected. The data were analyzed using Microsoft Excel and SPSS.

According to the results of the study, from 1,000 articles, 989 of them (98.9 per cent) were mentioned at least once in different social media websites and tools. The most used altmetric source in highly cited articles was Mendeley (98.9 per cent), followed by Citeulike (79.8 per cent) and Wikipedia (69.4 per cent). Most Tweets, blog posts, Facebook posts, news stories, readers in Mendeley, Citeulike and Connotea and Wikipedia citations belonged to the article titled “Mastering the game of Go with deep neural networks and tree search”. The highest altmetric score was 3,135 which belonged to this paper. Most tweeters and articles’ readers were from the USA. The membership type of the tweeters was public membership. In terms of fields of study, most readers were PhD students in Agricultural and Biological Sciences. Finally, the results of Spearman’s Correlation revealed positive significant statistical correlation between all altmetric indicators and received citations of highly cited articles (p-value = 0.0001).

The results of this study can help researchers, editors and editorial boards of journals better understand the importance and benefits of using social media and tools to publish articles.

Altmetrics is a relatively new field, and in particular, there are not many studies related to the presence of articles in various social media until now. Accordingly, in this study, a comprehensive altmetric analysis was carried out on 1000 most-cited articles of one of the world's most reliable journals.

The Journal of Intellectual Capital (JIC) celebrated its 20th anniversary in 2020. Therefore, the present study aims to provide a general overview of the history and key trends in this journal during 2000–2019.

Two types of citation and textual data during a 20-year journal period were retrieved from the Scopus database. The citation structures and contents were explored based on a combination of bibliometric analysis, altmetric analysis and text mining. The journal themes and trends of their changes were analyzed through citation bursts, mapping and topic modeling. To make a better comparison, the text mining process for the topic modeling of the IC field was performed in addition to the topic modeling of JIC.

Bibliometric analysis indicated that JIC has experienced a remarkable growth in terms of the number of publications and citations over the last 20 years. The results indicated that JIC plays a significant role among IC researchers. Additionally, a large number of researchers, institutes and countries have made contributions to this journal and cited its research papers. Altmetric analysis showed that JIC has been shared in different social media such as Twitter, Facebook, Wikipedia, Mendeley, Citeulike, news and blogs. Text mining abstract of JIC articles indicated that “measurement,” “financial performance” and “IC reporting” have the relative prevalence with increasing trends over the past 20 years. In addition, “research trends” and “national and international studies” had a stable trend with low thematic share.

The findings have important implications for the JIC editorial team in order to make informed decisions about the further development of JIC as well as for IC researchers and practitioners to make more valuable contributions to the journal.

Using bibliometric analysis, altmetric analysis and text mining, this study provided a systematic and comprehensive analysis of JIC. The simultaneous use of these methods provides an interesting, unique and suitable capacity to analyze the journals by considering their various aspects.

The purpose of this paper is to investigate the usage, captures, mentions, social media and citations of highly cited papers of Library and information science (LIS).

This study is quantitative research that was conducted using scientometrics and altmetrics indicators. The research sample consists of LIS classic papers. The papers contain highly cited papers of LIS that are introduced by Google Scholar. The research data have been gathered from Google Scholar, Scopus and Plum Analytics Categories. The data analysis has been done by Excel and SPSS applications.

The data indicate that among the highly cited articles of LIS, the highest score regarding the usage, captures, mentions and social media and the most abundance of citations belong to “Citation advantage of open access articles” and “Usage patterns of collaborative tagging systems.” Based on the results of Spearman statistical tests, there is a positive significant correlation between Google Scholar Citations and all studied indicators. However, only the correlation between Google Scholar Citations with capture metrics (p-value = 0.047) and citation metrics (p-value = 0.0001) was statistically significant.

Altmetrics indicators can be used as complement traditional indicators of Scientometrics to study the impact of papers. Therefore, the Altmetrics knowledge of LIS researchers and experts and practicing new studies in this field will be very important.

This paper aims to explore the characteristics of knowledge diffusion in library and information science (LIS) to reveal the impact of knowledge in LIS on other disciplines and the disciplinary status of LIS.

Taking the 573 highly cited papers (HCP) of LIS during the years 2000–2019 in Web of Science and 85,638 papers citing them from non-LIS disciplines as the analysis object, this paper analysed the disciplines to which the citing papers belonged regarding the Biglan model, and the topics and their characteristics of the citing disciplines using latent Dirichlet allocation topic clustering.

The results showed that the knowledge in LIS was exported to multiple disciplines and topics. (1) Citations from other disciplines were overall increasing, and the main citing disciplines, mainly from applied science disciplines, were medicine, computer science, management, economics, education, sociology, psychology, journalism and communication, earth science, engineering, biology, political science, chemistry and agronomy. However, those disciplines had fewer citations to LIS during for the years from 2000 to 2004, with rapid growth in the next three time periods. (2) The citing papers had various topics and showed an increasing trend in quantity. Moreover, topics of different disciplines from 2000 to 2019 had various characteristics.

From the perspective of discipline and topic, this study analyses papers citing the HCP of LIS from non-LIS disciplines, revealing the impact of knowledge in LIS on other disciplines.

The aim of this paper is to scrutinize the accessibility and decay of web references (URLs) cited in five open access social sciences journals indexed by ISI.

After acquiring all the papers published by these journals during 2002‐2007, their web citations were extracted and analyzed from an accessibility point of view. Moreover, for initially missed citations complementary pathways such as using Internet Explorer and the Google search engine were employed.

The study revealed that at first check 73 per cent of URLs are accessible, while 27 per cent have disappeared. It is notable that the rate of accessibility increased to 89 per cent and the rate of decay decreased to 11 per cent after using complementary pathways. The “.net” domain, with an availability of 96 per cent (a decay of 4 per cent) has the greatest stability and persistence among all domains, while the most stable file format is PDF, with an availability of 93 per cent (a decay of 7 per cent).

Given the inevitable, destructive and progressing decay phenomenon in web citations, after estimating the extent of this decay for five journals using innovative and standard methods, this paper suggests recommendations for preventing it. The paper carries research value for web content providers, publishers, editors, authors and researchers.

The present study compares the use of web citations as references in leading scholarly journals in Library and Information Science (LIS) and Communication and Media Studies (CMS). A total of 20 journals (each 10 from LIS and CMS) were selected based on the publishing history and reputation published between 2008 and 2017.

The present study compares the use of web citations as references in leading scholarly journals in LIS and CMS. A PHP script was used to crawl the Uniform Resource Locators (URLs) collected from the reference list. A total of 12,251 articles were downloaded and 555,428 references were extracted. Of the 555,428 references, 102,718 web citations were checked for their accessibility.

The research findings indicated that 76.90% URLs from LIS journals and 84.32% URLs from Communication and Media Studies journals were accessible and others were rotten. The majority of errors were due to HTTP 404 error code (not found) in both the disciplines. The study also tried to retrieve the rotten URLs through Time Travel, which revived 61.76% rotten URLs in LIS journal articles and 65.46% in CMS journal articles.

This is an in-depth and comprehensive comparative study on the availability of web citations in LIS and CMS journals articles spanning a period of 10 years. The findings of the study will be helpful to authors, publishers, and editorial staff to ensure that web citations will be accessible in the future.