Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.024
Mayur Makawana, Rupa G. Mehta
As part of the research process, relevant documents are identified to keep up with the latest advancements in the domain. Document recommendation systems are used by researchers as a means of accomplishing this goal. Textual content, collaborative filtering, and citation information-based approaches are among the proposed approaches for the recommendation systems. Content-based techniques take advantage of the entire text of papers and produce more promising results, but comparing input document text data to every document in the dataset is not practical for the content-based recommender system. This study looks into the possibility of using bibliographic data to reduce the number of comparisons. The proposed system is based on the assumption that two scientific papers are semantically connected if they are co-cited more frequently than by chance. The likelihood of co-citation, also known as semantic relatedness, can be used to quantify this connection. This work presents a new way to distribute the weight among connected scholarly documents based on a semantic relatedness score. Our proposed solution eliminates a substantial amount of needless text comparisons for the system by gathering scholarly document pairs with high likelihood values and using them as a search area for the content-based recommender system. By spreading the co-citation relationship out to certain distances, the proposed approach can find relevant documents that are not found by traditional co-citation searches. The results reveal that the system is capable of reducing computations by a significant margin and of detecting false positive situations in content comparison using Doc2vec.
{"title":"Discovering Search Space Using M-distance Clustering of Semantic Relatedness Based Weighted Network for the Content-based Recommender System","authors":"Mayur Makawana, Rupa G. Mehta","doi":"10.5530/jscires.12.2.024","DOIUrl":"https://doi.org/10.5530/jscires.12.2.024","url":null,"abstract":"As part of the research process, relevant documents are identified to keep up with the latest advancements in the domain. Document recommendation systems are used by researchers as a means of accomplishing this goal. Textual content, collaborative filtering, and citation information-based approaches are among the proposed approaches for the recommendation systems. Content-based techniques take advantage of the entire text of papers and produce more promising results, but comparing input document text data to every document in the dataset is not practical for the content-based recommender system. This study looks into the possibility of using bibliographic data to reduce the number of comparisons. The proposed system is based on the assumption that two scientific papers are semantically connected if they are co-cited more frequently than by chance. The likelihood of co-citation, also known as semantic relatedness, can be used to quantify this connection. This work presents a new way to distribute the weight among connected scholarly documents based on a semantic relatedness score. Our proposed solution eliminates a substantial amount of needless text comparisons for the system by gathering scholarly document pairs with high likelihood values and using them as a search area for the content-based recommender system. By spreading the co-citation relationship out to certain distances, the proposed approach can find relevant documents that are not found by traditional co-citation searches. The results reveal that the system is capable of reducing computations by a significant margin and of detecting false positive situations in content comparison using Doc2vec.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"7 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85691159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.040
Fati Tahiru, Steven. Parbanath, Samuel Agbesi
This paper aims to comprehensively review the present state and research trends in predictive systems in higher education. It also addresses the research contribution of countries in Machine Learning-based predictive systems in higher education to depict the research landscape given the growing number of related publications. A bibliometric analysis of publications on predictive systems in education published in the Scopus Database from 2015 to 2022 was conducted. The dataset obtained covered the contribution of authors, affiliations, countries, themes and trends in the field of Machine Learning-based predictive systems in higher education. A total of 72 publications with 3408 cited references were collected from Scopus for the bibliometric analysis. The technique used for the bibliometric analysis included performance analysis and science mapping. Research on Machine Learning-based predictive systems has been widely published from 2020 to 2022. Researchers in China, Belgium, Spain, India, and Korea were most active in researching Machine Learning-based predictive systems in education. However, international collaborations have remained infrequent except for the few involving Australia, Belgium, and Canada. There is a lack of research in the subject area in Africa. This study illustrates the intellectual landscape of Machine Learning-based predictive systems in higher education and the field's evolution and emerging trends. The findings highlight the area of research concentration and the most recent developments and suggest future research collaborations on a larger scale as well as additional research on the implementation of
{"title":"Machine Learning-based Predictive Systems in Higher Education: A Bibliometric Analysis","authors":"Fati Tahiru, Steven. Parbanath, Samuel Agbesi","doi":"10.5530/jscires.12.2.040","DOIUrl":"https://doi.org/10.5530/jscires.12.2.040","url":null,"abstract":"This paper aims to comprehensively review the present state and research trends in predictive systems in higher education. It also addresses the research contribution of countries in Machine Learning-based predictive systems in higher education to depict the research landscape given the growing number of related publications. A bibliometric analysis of publications on predictive systems in education published in the Scopus Database from 2015 to 2022 was conducted. The dataset obtained covered the contribution of authors, affiliations, countries, themes and trends in the field of Machine Learning-based predictive systems in higher education. A total of 72 publications with 3408 cited references were collected from Scopus for the bibliometric analysis. The technique used for the bibliometric analysis included performance analysis and science mapping. Research on Machine Learning-based predictive systems has been widely published from 2020 to 2022. Researchers in China, Belgium, Spain, India, and Korea were most active in researching Machine Learning-based predictive systems in education. However, international collaborations have remained infrequent except for the few involving Australia, Belgium, and Canada. There is a lack of research in the subject area in Africa. This study illustrates the intellectual landscape of Machine Learning-based predictive systems in higher education and the field's evolution and emerging trends. The findings highlight the area of research concentration and the most recent developments and suggest future research collaborations on a larger scale as well as additional research on the implementation of","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"3 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89721993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.035
Muhammad Farid Azlan Halmi, Mohd Amirul Faiz Zulkifli, Kamal Hisham Kamarul Zaman
The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) is a promising molecular tool that has revolutionised genome editing and was recognised with the Nobel Prize in Chemistry in 2020. This study assesses the scientific productivity and knowledge structure in the scientific domain of CRISPR-Cas9 genome editing up to 2022. A total of 12,799 publications were retrieved from the Science Citation Index Expanded (SCIE) database within the Web of Science (WoS), employing related keyword searches. The records were published by authors from 107 countries in 1,731 journals. Of the total scientific publications, 499,895 total citations were found, with 39.06 average citations per publication. The United States of America dominated the research and is currently the global leader in this area with the most publications and prolific top institutions. Visualisation analysis for mapping research trends based on co-occurrences of keywords was done using VOSviewer revealing six clusters of research themes comprising; 1) conception and fundamental development; 2) gene therapy and drug delivery; 3) cancer biology; 4) plant biotechnology; 5) livestock breeding, and; 6) synthetic biology and metabolic engineering. Nanoparticle-based delivery of CRISPR-Cas9 is gaining academic attention, while CRISPR-Cas9 application in synthetic biology and metabolic engineering has progressed recently and becoming the current research interest.
簇状规则间隔短回文重复序列(CRISPR)相关蛋白9 (CRISPR- cas9)是一种很有前途的分子工具，它彻底改变了基因组编辑，并在2020年获得了诺贝尔化学奖。本研究评估了截至2022年CRISPR-Cas9基因组编辑科学领域的科学生产力和知识结构。采用相关关键字检索方法从Web of Science (WoS)的SCIE数据库中检索到12799篇文献。这些记录由来自107个国家的作者发表在1731份期刊上。在全部科学出版物中，共被引用499895次，平均被引用39.06次。美国主导了这一研究，目前是该领域的全球领导者，拥有最多的出版物和多产的顶级机构。使用VOSviewer对基于关键词共现的研究趋势进行可视化分析，揭示了六个研究主题集群，包括;1)概念和基本发展;2)基因治疗与药物传递;3)癌症生物学;4)植物生物技术;(五)牲畜养殖;6)合成生物学与代谢工程。近年来，CRISPR-Cas9在合成生物学和代谢工程领域的应用取得了新的进展，成为当前的研究热点。
{"title":"CRISPR-Cas9 Genome Editing: A Brief Scientometric Insight on Scientific Production and Knowledge Structure","authors":"Muhammad Farid Azlan Halmi, Mohd Amirul Faiz Zulkifli, Kamal Hisham Kamarul Zaman","doi":"10.5530/jscires.12.2.035","DOIUrl":"https://doi.org/10.5530/jscires.12.2.035","url":null,"abstract":"The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated protein 9 (CRISPR-Cas9) is a promising molecular tool that has revolutionised genome editing and was recognised with the Nobel Prize in Chemistry in 2020. This study assesses the scientific productivity and knowledge structure in the scientific domain of CRISPR-Cas9 genome editing up to 2022. A total of 12,799 publications were retrieved from the Science Citation Index Expanded (SCIE) database within the Web of Science (WoS), employing related keyword searches. The records were published by authors from 107 countries in 1,731 journals. Of the total scientific publications, 499,895 total citations were found, with 39.06 average citations per publication. The United States of America dominated the research and is currently the global leader in this area with the most publications and prolific top institutions. Visualisation analysis for mapping research trends based on co-occurrences of keywords was done using VOSviewer revealing six clusters of research themes comprising; 1) conception and fundamental development; 2) gene therapy and drug delivery; 3) cancer biology; 4) plant biotechnology; 5) livestock breeding, and; 6) synthetic biology and metabolic engineering. Nanoparticle-based delivery of CRISPR-Cas9 is gaining academic attention, while CRISPR-Cas9 application in synthetic biology and metabolic engineering has progressed recently and becoming the current research interest.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"42 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85777225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.023
Solanki Gupta, Vivek Kumar Singh
The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas.
人工智能(AI)在人类生活各个领域的普遍应用导致了人工智能研究的惊人增长。人工智能的研究成果在过去十年中迅速增长。虽然一些科学计量学研究已经注意到出版物的增长，但实际上没有研究可以从开展和发表人工智能研究的领域和期刊的增长方面描述出版物的增长。本文试图利用Bradford生产率定律的Leimkuhler模型对人工智能研究发表进行定量估计，以填补这一研究空白。2011年至2020年期间在Web of Science索引的出版物用于分析。分析利用生产力分布及其特征解释了人工智能研究语料库的变化。定量研究结果支持了这样一种观点，即人工智能研究不仅在数量上有所增加，而且在更广泛的领域得到了应用。
{"title":"Quantitative Estimation of Trends in Artificial Intelligence Research: A Study of Bradford Distributions using Leimkuhler Model","authors":"Solanki Gupta, Vivek Kumar Singh","doi":"10.5530/jscires.12.2.023","DOIUrl":"https://doi.org/10.5530/jscires.12.2.023","url":null,"abstract":"The ubiquitous applications of Artificial Intelligence (AI) in various domains of human life have resulted in a phenomenal increase in AI research. The research output in AI has grown rapidly during the last decade. While some scientometric studies have noted this growth in publications, there are virtually no studies that could characterize the growth in publications in terms of the increase in domains and journals in which AI research is being carried out and published. This article makes an attempt to fill this research gap by using the Leimkuhler model of Bradford’s law of productivity to produce quantitative estimates of AI research publishing. Publications indexed in Web of Science for the period 2011 to 2020 are used for analysis. The analysis explains the variation in the corpus of AI research using productivity distribution and its characteristics. The quantitative findings support the idea that AI research has not only increased in volume but also in terms of applications to a wider list of areas.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"24 3 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90784249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.032
A. Kshitij, Jaideep Ghosh
To combat the effects of climate change and meet the need for clean energy, the global power sector, over the past few decades, has been undergoing a major transformation for which all possible renewable energy sources are currently being utilized. To achieve sustainable growth, India, like many other countries, is also in the process of energy transition, aiming to shift to renewable energy-based power generation. In this transition, research in Ocean Renewable Energy (ORE) technologies is rising to rapid prominence. This study examines the state of ORE research in India and compares it with global research activities in this field using a graph-theoretical framework for collaboration co-authorship networks in ORE using bibliometric data on published scholarly articles indexed in two well-known electronic databases covering two 10-year windows: 1999-2008 and 2009-2018, inclusive. A strategic analysis of a number of metrics characterizing the networks’ large-scale structures reveals that the Indian network is highly fragmented, resulting in a singular dearth of large-scale connections for Indian ORE researchers. We recommend effective research policies to improve knowledge generation and dissemination in ORE research collaboration in India (and many other countries in similar situations), based on our findings for Indian networks and pertinent parallels with global ORE. With growing concerns about sustainable energy utilization, our study has policy implications for pressing issues of energy demands in the country.
{"title":"Ocean Renewable Energy: A Comparative Study of Indian and Global Collaborative Research for Sustainability and Policy Implications","authors":"A. Kshitij, Jaideep Ghosh","doi":"10.5530/jscires.12.2.032","DOIUrl":"https://doi.org/10.5530/jscires.12.2.032","url":null,"abstract":"To combat the effects of climate change and meet the need for clean energy, the global power sector, over the past few decades, has been undergoing a major transformation for which all possible renewable energy sources are currently being utilized. To achieve sustainable growth, India, like many other countries, is also in the process of energy transition, aiming to shift to renewable energy-based power generation. In this transition, research in Ocean Renewable Energy (ORE) technologies is rising to rapid prominence. This study examines the state of ORE research in India and compares it with global research activities in this field using a graph-theoretical framework for collaboration co-authorship networks in ORE using bibliometric data on published scholarly articles indexed in two well-known electronic databases covering two 10-year windows: 1999-2008 and 2009-2018, inclusive. A strategic analysis of a number of metrics characterizing the networks’ large-scale structures reveals that the Indian network is highly fragmented, resulting in a singular dearth of large-scale connections for Indian ORE researchers. We recommend effective research policies to improve knowledge generation and dissemination in ORE research collaboration in India (and many other countries in similar situations), based on our findings for Indian networks and pertinent parallels with global ORE. With growing concerns about sustainable energy utilization, our study has policy implications for pressing issues of energy demands in the country.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"39 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75628892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.038
Zong-Yu Wang, Zhi-Chao Zhou, Jie Zheng, Zhu-Kai Cong, Xi Zhu
{"title":"Bibliometric Analysis of Literature on Acute Respiratory Distress Syndrome Treatments Published Between 2000 and 2019","authors":"Zong-Yu Wang, Zhi-Chao Zhou, Jie Zheng, Zhu-Kai Cong, Xi Zhu","doi":"10.5530/jscires.12.2.038","DOIUrl":"https://doi.org/10.5530/jscires.12.2.038","url":null,"abstract":"","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136043082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.047
K. C. Garg, Ritu Nagpal
This brief communication is the outcome of the prevailing confusion and the role of editors of several prestigious journals published in India in different disciplines of science and social science including the discipline of library and information science. These journals use different Plagiarism Detection Tools (PDTs) or Plagiarism Detection Software (PDS) to assess the similarity score of the submitted manuscript. These PDTs are helpful to avoid questions raised on the academic integrity of the submitted manuscript. For every submission, the editor of the journal generates a similarity report and communicates the results of the similarity index to the scholars verbatim. It is not judicious for the editor of the journal to simply rely on the percentage of similarity index suggested by the PDT. Human intervention is required to rule out the facts by a thorough inspection of every single matching. Also, an acceptable percentage of similarity for a manuscript needs a critical analysis. Based on the set guidelines of the academic regulatory bodies, an acceptable percentage of similarity for a manuscript is considered as minor or level 0 if it is 10%. The present communication draws attention towards this malice as a lot of time of the author/scholar is devoted to incorporate small changes which do not serve any useful purpose to the manuscript and the journal.
{"title":"Role of the Editor in Limiting Plagiarism or Similarity in Scholarly Journal Manuscripts","authors":"K. C. Garg, Ritu Nagpal","doi":"10.5530/jscires.12.2.047","DOIUrl":"https://doi.org/10.5530/jscires.12.2.047","url":null,"abstract":"This brief communication is the outcome of the prevailing confusion and the role of editors of several prestigious journals published in India in different disciplines of science and social science including the discipline of library and information science. These journals use different Plagiarism Detection Tools (PDTs) or Plagiarism Detection Software (PDS) to assess the similarity score of the submitted manuscript. These PDTs are helpful to avoid questions raised on the academic integrity of the submitted manuscript. For every submission, the editor of the journal generates a similarity report and communicates the results of the similarity index to the scholars verbatim. It is not judicious for the editor of the journal to simply rely on the percentage of similarity index suggested by the PDT. Human intervention is required to rule out the facts by a thorough inspection of every single matching. Also, an acceptable percentage of similarity for a manuscript needs a critical analysis. Based on the set guidelines of the academic regulatory bodies, an acceptable percentage of similarity for a manuscript is considered as minor or level 0 if it is 10%. The present communication draws attention towards this malice as a lot of time of the author/scholar is devoted to incorporate small changes which do not serve any useful purpose to the manuscript and the journal.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"3 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82802243","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-06DOI: 10.5530/jscires.12.2.028
Thinh-Van Vu
This study aims to provide an overview of the state-of-the-art research at the intersection of how organizations and employees perceive and respond to a crisis. By using scientific mapping, the study also seeks to ascertain the intellectual structure of the knowledge base and highlight trends on the topic. Using the Web of Science (WoS) database, 546 publications were chosen for further examination. The research employed bibliometric indicators such as authors, documents, journals, field publications, and countries. In addition, VOSviewer was used to perform science mapping analyses such as co-word and co-citation. This study finds that scholars are increasingly interested in this topic, as evidenced by a growing trend in the academic literature. The six clusters in co-citation networks are identified as the pillars of the theoretical foundation of research on this topic. Moreover, by classifying keywords into seven themes, the research explores thematic trends on this topic. The results find that “Remote work, work-family conflict, and work-life balance” emerged as an emerging trend within 2020–2022. Furthermore, the findings reveal several new keywords have appeared in the research fields since the COVID-19 outbreak. This study indicates the most important emerging themes, research topics, and critical debates and then outlines potential avenues for future research.
本研究旨在概述组织和员工如何感知和应对危机的交叉点的最新研究。通过使用科学地图，该研究还试图确定知识库的知识结构，并突出该主题的趋势。利用Web of Science (WoS)数据库，选择546篇出版物进行进一步检查。该研究采用了文献计量指标，如作者、文献、期刊、领域出版物和国家。此外，使用VOSviewer进行共词和共引等科学制图分析。本研究发现，学者们对这一话题的兴趣日益浓厚，学术文献也呈现出日益增长的趋势。共被引网络中的六大集群是本课题研究的理论基础支柱。此外，通过将关键词划分为七个主题，研究了该主题的主题趋势。结果发现，“远程工作、工作-家庭冲突和工作-生活平衡”在2020-2022年期间成为一种新兴趋势。此外，研究结果显示，自新冠肺炎疫情爆发以来，研究领域出现了几个新的关键词。本研究指出了最重要的新兴主题、研究课题和关键争论，然后概述了未来研究的潜在途径。
{"title":"How do Organizations and Employees Perceive and Respond to a Crisis: A Co-citation and Co-word Analysis","authors":"Thinh-Van Vu","doi":"10.5530/jscires.12.2.028","DOIUrl":"https://doi.org/10.5530/jscires.12.2.028","url":null,"abstract":"This study aims to provide an overview of the state-of-the-art research at the intersection of how organizations and employees perceive and respond to a crisis. By using scientific mapping, the study also seeks to ascertain the intellectual structure of the knowledge base and highlight trends on the topic. Using the Web of Science (WoS) database, 546 publications were chosen for further examination. The research employed bibliometric indicators such as authors, documents, journals, field publications, and countries. In addition, VOSviewer was used to perform science mapping analyses such as co-word and co-citation. This study finds that scholars are increasingly interested in this topic, as evidenced by a growing trend in the academic literature. The six clusters in co-citation networks are identified as the pillars of the theoretical foundation of research on this topic. Moreover, by classifying keywords into seven themes, the research explores thematic trends on this topic. The results find that “Remote work, work-family conflict, and work-life balance” emerged as an emerging trend within 2020–2022. Furthermore, the findings reveal several new keywords have appeared in the research fields since the COVID-19 outbreak. This study indicates the most important emerging themes, research topics, and critical debates and then outlines potential avenues for future research.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"167 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83347029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.5530/jscires.12.1.019
Z. Zamrudi
Introduction: Counterproductive knowledge behaviour is considered to negatively impact all organizations, either in business or public institutions. Objectives: This study aims to provide a comprehensive picture of knowledge management, integrating three counterproductive knowledge behaviours: knowledge hoarding, knowledge withholding, and knowledge hiding. Materials and Methods: This study uses a bibliometric approach using 337 documents from the Scopus database to understand the field development behind counterproductive knowledge behaviours. The data analysis involves the evaluation of performance analysis and thematic mapping. The performance analysis is aimed to understand the pioneering authors and manuscripts within the field, while the scientific maps aim to depict the thematic development of current fields. The performance analysis results discover pioneering authors, trending topics, prominent sources and articles, and country-wise performance. Results: The performance analysis indicates growing interest since 2011 mainly published in “knowledge” theme journal, mainly authored by Chinese researcher. However, from the authorship results, this research also pin-point pioneering author within the field. The results from scientific map analysis indicate different concepts between knowledge hoarding, knowledge withholding, and knowledge hiding while at the same time discovering the position of knowledge sharing within these three concepts. This study also discovers several basic theories on counterproductive knowledge behaviour. Conclusion: This research contributes to the scientific community by comprehensively combining the performance and scientific map analysis to measure the research development in counterproductive knowledge behaviour. Additionally, this paper provide future research agenda within the fields inviting future researcher to explore any potential theoretical integration, model integration such as involving technological aspect.
{"title":"A Pathway to Counterproductive Knowledge Behaviour: Integrating Knowledge Hoarding, Knowledge Withholding, and Knowledge Hiding","authors":"Z. Zamrudi","doi":"10.5530/jscires.12.1.019","DOIUrl":"https://doi.org/10.5530/jscires.12.1.019","url":null,"abstract":"Introduction: Counterproductive knowledge behaviour is considered to negatively impact all organizations, either in business or public institutions. Objectives: This study aims to provide a comprehensive picture of knowledge management, integrating three counterproductive knowledge behaviours: knowledge hoarding, knowledge withholding, and knowledge hiding. Materials and Methods: This study uses a bibliometric approach using 337 documents from the Scopus database to understand the field development behind counterproductive knowledge behaviours. The data analysis involves the evaluation of performance analysis and thematic mapping. The performance analysis is aimed to understand the pioneering authors and manuscripts within the field, while the scientific maps aim to depict the thematic development of current fields. The performance analysis results discover pioneering authors, trending topics, prominent sources and articles, and country-wise performance. Results: The performance analysis indicates growing interest since 2011 mainly published in “knowledge” theme journal, mainly authored by Chinese researcher. However, from the authorship results, this research also pin-point pioneering author within the field. The results from scientific map analysis indicate different concepts between knowledge hoarding, knowledge withholding, and knowledge hiding while at the same time discovering the position of knowledge sharing within these three concepts. This study also discovers several basic theories on counterproductive knowledge behaviour. Conclusion: This research contributes to the scientific community by comprehensively combining the performance and scientific map analysis to measure the research development in counterproductive knowledge behaviour. Additionally, this paper provide future research agenda within the fields inviting future researcher to explore any potential theoretical integration, model integration such as involving technological aspect.","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"65 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89605202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-17DOI: 10.5530/jscires.12.1.009
Farideh Osareh, Parastoo Parsaei-Mohammadi, A. Farajpahlou, Farajolah Rahimi
University ranking systems are among the topics of interest in scientometric studies. This study aims to identify and rank the most important criteria and indicators of global, regional, and national university ranking systems. In this descriptive study, the criteria and indicators of 34 global rankings, 23 regional rankings, 88 national rankings, and 145 university rankings were reviewed. Criteria and indicators of each ranking system were written on special worksheets. Data were analyzed using descriptive statistics such as frequency, frequency percentage, and cumulative frequency. After combining the identified criteria and indicators, 17 criteria and 397 indicators were extracted. The results showed that in academic ranking systems, the criteria of research, education, students, financial factors, internationalization, and regional interactions emphasized more than other cases. today, universities and institutions of higher education will no longer be able to carry out the new missions of scientific societies in the process of producing knowledge and wealth by using only the traditional functions of the university, namely education and research. This article shows the current trends in the national, regional, and international ranking of universities, which can provide a perspective for the development of ranking systems and increase the quality of
{"title":"A Comparative Study of Criteria and Indicators of Local, Regional, and National University Ranking Systems","authors":"Farideh Osareh, Parastoo Parsaei-Mohammadi, A. Farajpahlou, Farajolah Rahimi","doi":"10.5530/jscires.12.1.009","DOIUrl":"https://doi.org/10.5530/jscires.12.1.009","url":null,"abstract":"University ranking systems are among the topics of interest in scientometric studies. This study aims to identify and rank the most important criteria and indicators of global, regional, and national university ranking systems. In this descriptive study, the criteria and indicators of 34 global rankings, 23 regional rankings, 88 national rankings, and 145 university rankings were reviewed. Criteria and indicators of each ranking system were written on special worksheets. Data were analyzed using descriptive statistics such as frequency, frequency percentage, and cumulative frequency. After combining the identified criteria and indicators, 17 criteria and 397 indicators were extracted. The results showed that in academic ranking systems, the criteria of research, education, students, financial factors, internationalization, and regional interactions emphasized more than other cases. today, universities and institutions of higher education will no longer be able to carry out the new missions of scientific societies in the process of producing knowledge and wealth by using only the traditional functions of the university, namely education and research. This article shows the current trends in the national, regional, and international ranking of universities, which can provide a perspective for the development of ranking systems and increase the quality of","PeriodicalId":43282,"journal":{"name":"Journal of Scientometric Research","volume":"35 1","pages":""},"PeriodicalIF":0.8,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86858367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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