Asian Social Science; Vol. 9, No. 5; 2013 ISSN 1911-2017 E-ISSN 1911-2025 Published by Canadian Center of Science and Education A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases
Arezoo Aghaei Chadegani1, Hadi Salehi2, Melor Md Yunus3, Hadi Farhadi4, Masood Fooladi1, Maryam Farhadi1 & Nader Ale Ebrahim5 1 Department of Accounting, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran 2 Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3 Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Malaysia 4 School of Psychology and Human Development, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia 5 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia Correspondence: Arezoo Aghaei Chadegani, Department of Accounting, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran. Tel: 60-17-319-1093. E-mail: [email protected]
Received: February 11, 2013 Accepted: March 18, 2013 Online Published: April 27, 2013 doi:10.5539/ass.v9n5p18 URL: http://dx.doi.org/10.5539/ass.v9n5p18
Abstract Nowadays, the world’s scientific community has been publishing an enormous number of papers in different scientific fields. In such environment, it is essential to know which databases are equally efficient and objective for literature searches. It seems that two most extensive databases are Web of Science and Scopus. Besides searching the literature, these two databases used to rank journals in terms of their productivity and the total citations received to indicate the journals impact, prestige or influence. This article attempts to provide a comprehensive comparison of these databases to answer frequent questions which researchers ask, such as: How Web of Science and Scopus are different? In which aspects these two databases are similar? Or, if the researchers are forced to choose one of them, which one should they prefer? For answering these questions, these two databases will be compared based on their qualitative and quantitative characteristics. Keywords: web of science, scopus, database, citations, provenance, coverage, searching, citation tracking, impact factor, indexing, h-index, researcher profile, researcher ID 1. Introduction Web of Science (WOS) and Scopus are the most widespread databases on different scientific fields which are frequently used for searching the literature (Guz & Rushchitsky, 2009). WOS from Thomson Reuters (ISI) was the only citation database and publication which covers all domains of science for many years. However, Elsevier Science introduced the database Scopus in 2004 and it is rapidly become a good alternative (Vieira & Gomes, 2009). Scopus database is the largest searchable citation and abstract source of searching literature which is continually expanded and updated (Rew, 2009). WOS is challenged by the release of Scopus, an academic literature database which is built on a similar breadth and scale. WOS and Scopus are expensive products and it may not be feasible to obtain both of them. Therefore, by surfacing the new citation database, Scopus, scientific libraries have to decide about which citation database can best meet the requests of the consumers? The competition between WOS and Scopus databases is intense. This competition has led to improvements in the services offered by them. Recently, various papers have compared the coverage, features and citation analysis capabilities of WOS and Scopus (Bakkalbasi, 2006; Burnham, 2006; LaGuardia, 2005; Deis & Goodman, 2005; Dess, 2006; Li et al., 2010). These comparative studies of WOS and Scopus conclude that these two databases are permanently improving. They also conclude that the significant advantage of choosing one of these two sources depends on the particular subject’s area. Some researchers propose undertaking subject’s specific analysis to find out which database work best for specific fields or time period (Bar-Ilan et al., 2007; Bakkalbasi et al., 2006; Neuhaus & Daniel, 2008). Based on Lopez-Illescas et al. (2008), prior comparisons of these two
18 www.ccsenet.org/ass Asian Social Science Vol. 9, No. 5; 2013 databases have not exposed a clear winner. They believe that the advantage of one database over another one depends on what explicitly will be analyzed, the scientific field and also period of analysis. Due to this argument, the existing issue is: which database is superior to use? Which one should you subscribe to? The answer to this question is full of equivocation. In this article, we will briefly analyze the journal evaluation capabilities of WOS and Scopus databases. In fact, this article concentrates on features and capabilities of each product and these databases will be compared from different aspects such as: 1) Provenance and coverage 2) Searching and analysis of results 3) Citation tracking and citation analysis 4) Forming and costs 5) Impact factors 6) Indexing (h-index) 7) Researcher profile and ID tools. Prior studies which compare these databases will be reviewed in this article. 2. Provenance and Coverage Web of Science (WOS), products from “Thomson Reuters Institute of Scientific Information” (ISI), arises from the Science Citation Index created by Eugene Garfield in 1960s. WOS includes above 10,000 journals and comprises of seven different citation databases including different information collected from journals, conferences, reports, books and book series. WOS citation databases are Social Sciences Citation Index (SSCI), Science Citation Index Expanded (SCI Expanded), Conference Proceedings Citation Index Science (CPCI-S), Arts and Humanities Citation Index (A&HCI) and Conference Proceedings Citation Index-Social Sciences and Humanities (CPCI-SSH). It has also two chemistry databases named Index Chemicus (IC) and Current Chemical Reactions (CCR-Expanded). Since WOS is the oldest citation database, it has strong coverage with citation data and bibliographic data which goes back to 1900 (Boyle & Sherman, 2006). WOS claims it has the most depth and the most quality however, Scopus burst in research domain in 2004 which claims to have the biggest database with the wide range of records. Scopus, officially named SciVerse Scopus, has introduced by Elsevier in November 2004 to the information market. Scopus is the largest database existing on the market for multidisciplinary scientific literatures (Kuchi, 2004). Scopus covers more than 49 million records including trade publications, open-access journals, and book series. Almost 80% of these records include abstract. It contains 20,500 peer-reviewed journals from 5,000 publishers, together with 1200 Open Access journals, over 600 Trade Publications, 500 Conference Proceedings and 360 book series from all areas of science (Rew, 2009). Scopus offers the new sorting and refining feature for researchers to access above 27 million citations and abstracts going back to 1960s (Boyle & Sherman, 2006). Most of institutions in all over the world such as Latin America, Europe, North America, Australia, Asia and the Middle East believe Scopus have positive influence on their researches. Based on Scopus report more than 50% of its information are from the Middle East, Europe and Africa. Boyle and Sherman (2006) believe that choosing Scopus is due to its quality of outcomes, time savings, ease of use and possible effect on research findings. Vieira and Gomes (2009) using the set of Portuguese universities compare tow databases. They conclude that Scopus provides 20% more coverage than WOS. Scopus covers broader journal range which is limited to recent articles comparing to WOS. Their results reveal that 2/3 of these records can be found in both databases but 1/3 of the records are only in one database. Vieira and Gomes (2009) suggest the coverage of one journal by Scopus database can have break. It means Scopus has a partial coverage for some journals. Checking the title list with journals indexed in Scopus shows that for different journals, the coverage by Scopus is not complete. The coverage comparison of WOS and Scopus databases provided by free online database comparison tool ‘JISC-ADAT’ is shown in figure 1.
Figure 1. JISC-ADAT coverage comparison of Web of Science and Scopus
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3. Searching and Analysis of Results WOS and Scopus are powerful databases which provide different searching and browsing options (Lopez-Illescas et al., 2008). The search options in both databases are the Standard Basic and Advanced. There are different searchable fields and several document types that permit the user to easily narrow their searching. Both databases sort the results by parameters such as; first author, cites, relevance and etc. The Refine Results section in both databases allows the user to quickly limit or exclude results by author, source, year, subject area, document type, institutions, countries, funding agencies and languages. The resulting documents provide a citation, abstract, and references at a minimum. Results may be printed, e-mailed, or exported to a citation manager. The results may also be reorganized according to the needs of the researcher by simply clicking on the headings of each column. Both databases provide users with the ability to set up a profile for personal settings and managing their saved searches and alerts all under one tab. A further user option is the ability to browse individual journals by simply clicking the Journals tab, locate the journal name and select the individual issue. Figure 2 shows the search screen of WOS and Scopus databases.
Figure 2. Web of Science and Scopus search screen
The analysis of search results is competitive characteristics of these two databases. Therefore, these databases attempt to improve their features to make searching easier for users. For example, WOS is able to create histograms and rankings according to different parameters. This feature is a stronger tool than that available in Scopus. Moreover, The Scientific Web Plus feature available through WOS enables user to see the search results by clicking on the icon in the search result page. Scientific Web Plus helps users to find scientifically concentrated open web content, author’s latest information and research scientists’ potential collaborators or
20 www.ccsenet.org/ass Asian Social Science Vol. 9, No. 5; 2013 competitors, and complements searches. Cited Reference Search (CRS) is another features in WOS for finding previously published and cited articles. WOS also has the “Related Records” feature to detect which authors are citing the same records. The similar feature in Scopus provides the option to discover documents related to the authors or keywords in a specific record. Another capability of Scopus is journal analyzer which allows users to evaluate journals based on the number of citations, articles published and percentage not cited. One of the weaknesses of Scopus is that it does not have references before 1996. However, one of the important features that introduced by Scopus to online databases is the presentation of faceted indexing. This faceted list of metadata has become a main delight to show in users’ training. But, WOS is one step upper than Scopus in representing metadata lists. In fact, search results in WOS are not presented and analyzed by author, year, subject and document types like Scopus, but also by language, institutions and countries in WOS. 4. Citation Tracking and Citation Analysis Citation tracking and analysis have been documented as an important factor for evaluating the influence or importance of specific record for a period of time. Researchers are able to evaluate records using citation data by WOS and Scopus Citation Tracker. In fact, Citation tracking indicates the number of times that a particular work, author or journal have been cited in other works. Citation tracking also allows you to track your own effect and the effect of your institution. Citation tracking offers complete information for other authors and organizations that have similar subject and identifies publications which have similar topics (Quint, 2006). Citation tracking to journal article is another frequent factor of comparing Scopus and WOS (Bergman, 2012). WOS analyzes the citations by document type, author, funding agency, country, organization’s name, language, grant number, publication year and research title. WOS citation reports provide two charts, representing citations in each year and published items in each year. This report also supplies the average citations per item, sum of times cited and the h-index number (Jacso, 2005). Scopus also provides citation information by date of records’ citation found about specific article or other records. Similar to WOS, it has a feature to find how many times other authors cited the same topic. Citation overview of Scopus is displayed by option of excluding self-citation with h-index in a graph format (Jacso, 2005). Therefore, both databases allow authors and researchers to discover how many citations an author or an article has received, to find the journals and authors who publish in your area of interest, to explore citations for a specific journal issue, volume or year; to review the citations and work of other authors (Rew, 2009). There are some studies that compare WOS and Scopus databases from the citation aspects and they find different results (Levine & Gil, 2009; Haddow & Genoni, 2010; Bergman, 2012). Figure 3 shows the citation reports in WOS and Scopus.
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Figure 3. Citation reports in Web of Science and Scopus
Levine Clark and Gil (2009) compare two citation databases for citing references from fifteen business and economic journals. They conclude that Scopus produced slightly higher citation numbers than WOS. Haddow and Genoni (2010) also find that Scopus have more citations counts than WOS and cover more journals. However, Bergman (2012) suggest that for the social welfare journals, Scopus commonly provides higher citation counts than WOS, but the ranking from the highest to the lowest citation count for each database results in similar outcome. Therefore, WOS and Scopus may likely provide correlated results in terms of ranking based on citation counts for other journals. Kulkarni et al. (2009) argue that both WOS and Scopus produce qualitatively and quantitatively diverse citations of articles published in medicine journals. However, Escalona et al. (2010) find that there is a high similarity between WOS and Scopus in other fields such as Chemical Engineering. Jacso (2005) and Falagas et al. (2008) compare the weaknesses and strengths of WOS and Scopus. They confirm that Scopus covers more journals and its analysis of citations is faster than WOS. However, WOS’s citation analysis provides more understandable graphics and is more detailed than Scopus’s citation analysis. Meho and Rogers (2008) investigate the differences of Scopus and WOS databases in the citation ranking, citation counting and h-index. They find that no significant differences exist between WOS and Scopus if they are compared only regarding their citation in journals. Levine Clark and Gil (2009) present the result of a comparison of two databases; WOS and Scopus for fifty journals in business and economics. Citations from both databases are analyzed to find out if one database is superior to another one, or whether Scopus could replace WOS. They conclude that researchers may intent to use alternative sources to get a more comprehensive picture of scholarly impact of one article. Bauer and Bakkalbasi (2005) compare the number of citations in WOS and Scopus for American Society Information Science and Technology journal for articles published from 1985 to 2000. They find no difference in citation numbers between WOS and Scopus for articles published in 2000. 5. Forming and Costs Regarding databases forming and shape, Burnham (2006) argues that Scopus is easy to follow even for beginner users comparing WOS. LaGuardia (2005) believes that Scopus draws one and attract researcher in at first look. It's easy to read because fonts and colours provide good contrast and it's less cluttered. Therefore, it is oriented more toward topics searching than WOS. Pricing is a critical issue as potential buyers pay attention to the cost and benefit outcomes of their decisions. A cost comparison between WOS and Scopus is extremely difficult because information about their cost is closely held by the database producers (Dess, 2006). The results in general show that databases pricing is a complex issue which depends on the size of the organization, discounts which they negotiated and other elements as well. Deis and Goodman (2005) claim that the estimation of WOS costs is about $100,000 per year for large organizations. However, the cost of Scopus database is about 85-95% of the cost of WOS for the same organizations. LaGuardia (2005) argues that WOS refused to provide pricing information but Scopus pricing is set according to annual subscription fee with unlimited usage. Pricing varies based on the size of the
22 www.ccsenet.org/ass Asian Social Science Vol. 9, No. 5; 2013 organizations from $20,000 to $120,000 per year. Regarding library budgets, it is highly difficult for any organization to pay for both of these databases. Therefore, the decision about which one to choose is taken by the sort of trade-offs of cost versus performance for each database. 6. Impact Factors The most powerful index which is used by WOS and Scopus databases for ranking journals is the journal’s impact factor (IF) (Garfield 2006). IF is calculated according to the citations of all articles published in the journal during two years (Guz & Rushchitsky, 2009). Levine Clark and Gil (2009) define IF as a ratio between citations and recent citable items published. Therefore, the journal’s IF is calculated through dividing the numbers of this year citation to the source item which is published in that journal during the prior two years. Researchers can obtain journals’ IF by using two online databases. WOS, Thomson Reuters’s product, annually publishes and analyzes indexed journals’ IF. Scopus, Elsevier publisher’s product, also records the information about journal citations, but does not report indexed journals’ IF, which could be obtained through manual calculation of journal citations or other tools (Chou, 2012). Nowadays, it is an honour for each journal to indicate the IF determined by the WOS. However, some scholars have expressed concern that WOS's IF does not accurately reflect the true influence of social work journals within the discipline partially due to the fact that many critical journals are left out of the ranking and calculations (Bergman, 2012). Lopez Illescas et al. (2008) find that for journals indexed in WOS and Scopus databases, those in WOS indicate higher IFs. On the contrary, those that are only covered by Scopus indicate lower IFs than if they are in both databases. Furthermore, they find that the differences between two databases regarding citation are much lower than the differences regarding coverage. Abrizah et al. (2012) compare the ranking, coverage, IF and subject categorization of Information Science journals based on data from WOS and Scopus. These comparisons are made according to factor scores reported in 2010 Journal Citation Reports. They use the Library of Congress Classification System to compare IF and subject categorization. They find that there is high degree of similarity in rank normalized IF of titles in both WOS and Scopus databases. Pislyakov (2009) reports that the IF ranking of indexed journals between WOS and Scopus is partially different. However, Gary and Hodkinson (2008) conclude that there is no significant difference between WOS and Scopus on journals’ IF ranking but these studies only focus on business and science field. Meho and Sugimoto (2009) investigate differences between WOS and Scopus to evaluate the scholarly impact of fields which focus on frequently research domains and institutions, citing journals, conference proceedings, and all citing countries. Their results indicate that when they assess the smaller citing entities such as journals, institutions and conference proceedings, both databases produce significantly different results. However, when they assess larger citing entities such as research domains and countries, they produce similar scholarly impact. 7. Indexing (H-Index) WOS database is a more scholarly source than Scopus database because of more indexing (Fingerman, 2006). Some indices are proposed by WOS and Scopus databases such as h-index, g-index, impact factor, the Eigen factor metric for journal ranking, source normalized impact per paper and relative measure index (Hirsch, 2005; Egghe, 2006; Bergstrom, 2007; Moed, 2010; Raj & Zainab, 2012). The h-index is a well known metric for assessing the researcher’s scientific effects (Raj & Zainab, 2012). For measuring h-index, the publication records of an author, the number of papers published during the selected number of years and the number of citations for each paper are considered (Moed, 2010). Glanzel (2006) believes that the advantage of h-index is that it combines the assessment of both the number of papers (quantity) and the impact or citations to these papers (quality). The h-index is automatically computed in both databases for every author and collections of articles which are selected by the user. Jacso (2011) verifies h-index, which is used by researchers to measure research productivity, for the Scopus database. The results reveal that 18.7 million records in Scopus have one or more cited references, which represents %42 of the whole database content. The cited references enhanced records ratio is rising from 1996 to 2009. Moreover, for other records of 23,455,354 published after 1995, the h-index is 1,339. Therefore, the total number of citations should be at least 1,792,921. For the whole Scopus database of 44.5 million records the h-index is 1,757 (Jacso, 2011). Bar-Ilan (2008) compares the h-index of a list of highly cited authors based on citations counts recovered from the WOS and Scopus. The results show that there is no difference when citation tool is used to calculate the h-index of scientists because the results of both databases are very close to each other. Moreover, it seems to be disciplinary differences in the coverage of the databases. The differences in citation numbers put science policy makers and promotion committees into the trouble.
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8. Researcher Profile and ID Tools Both WOS and Scopus databases make available an author search feature by distinguishing the author’s affiliation, subject and journals to classify variations for the same author. They also have some different features for researchers to upload their profile information. The Distinct Author Set is a discovery tool in WOS database which shows several papers which written by one author. In fact, WOS analyzes the citation data, like journals and subject areas for developing the distinct author set. However, in Scopus, there is a feature called The Author Identifier. The user can enter the author’s name and will have an exact match of whole name of author, as well as how his name appears in journal articles and other records. The Scopus Author Identifier (AI) matches author names according to their affiliation, source title, subject area, address and co-authors. Consequently, the Scopus AI helps researchers to get accurate and comprehensive information as quickly as possible without wasting time on searching through long list of author’s names. Moreover, the research ID feature in WOS allows users to upload their profile information (Vieira & Gomes, 2009). Researcher ID provides a solution to author ambiguity problem in the scholarly research community. Each member is assigned with unique identifier to enable researcher to manage their publication lists, track their times cited counts and h-index, identify potential collaborators and avoid author misidentification. Additionally, your Researcher ID information integrates with the Web of Knowledge and is ORCID compliant, allowing you to claim and showcase your publications from a single one account. Figure 4 shows Researcher ID feature in WOS.
Figure 4. Researcher ID in Web of Science
9. Conclusion Until 2004, the Web of Science (WOS) was the only international and multidisciplinary database available to obtain the literature of technology, science, medicine and other fields. However, Elsevier introduced Scopus which is become a good replacement (Vieira & Gomes, 2009). The information provide by these databases specify the active journals in covering current and relevant research as well as prominent in shaping potential research fields. The intense competition between these databases motivated researchers to compare them to identify their similarities and differences. A numerous researches compare these databases from different aspects. In this article, WOS and Scopus databases are compared based on qualitative and quantitative characteristics such as provenance, citations, searching and special features by reviewing prior studies. The comparison of WOS and Scopus discovers that WOS has strong coverage which goes back to 1990 and most of its journals written in English. However, Scopus covers a superior number of journals but with lower impact and limited to recent articles. Both databases allow searching and sorting the results by expected parameters such as first author, citation, institution and etc. regarding impact factor and h-index, different results obtained from prior studies. Although there is a high association between both databases, researchers interested to know why authors prefer one database over the other one. For further studies, it is suggested to investigate the perceptions of authors and researchers on both databases to find the reasons which make them to use one database more than the other one. It could be helped databases to improve their features to provide better facilities.
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Electronic physician; Volume 6, Issue 2, April-June 2014 http://www.ephysician.ir
Letter To Editor
Citation Frequency and Ethical Issue
Nader Ale Ebrahim
Ph.D. of Technology Management, Visiting Research Fellow, Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Kuala Lumpur, Malaysia
Corresponding Author: Dr. Nader Ale Ebrahim, Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Kuala Lumpur, Malaysia. Tel: +60.37967 7812, Fax: +60.37967 7813, Email: [email protected]
Keywords: citation, impact, ethics
Bibliographic Information of this article: [Nader Ale Ebrahim. Citation Frequency and Ethical Issue. Electronic Physician, 2014; 6 (2): 814-815]. (ISSN: 2008-5842). http://www.ephysician.ir Available online at: http://www.ephysician.ir/2014/814-815.pdf
This work is licensed under a Creative Commons Attribution 3.0 Unported License. Received: May.02.2014 Revised: 1st Revision: May.04.2014; 2nd Revision: May.10.2014 Accepted: May.10.2014 Published: May.11. 2014
Dear Editor: I read your publication ethics issue on “bogus impact factors” with great interest (1). I would like to initiate a new trend in manipulating the citation counts. There are several ethical approaches to increase the number of citations for a published paper (2). However, it is apparent that some manipulation of the number of citations is occurring (3, 4). Self-citations, “those in which the authors cite their own works” account for a significant portion of all citations (5). With the advent of information technology, it is easy to identify unusual trends for citations in a paper or a journal. A web application to calculate the single publication h-index based on (6) is available online (7, 8). A tool developed by Francisco Couto (9) can measure authors’ citation impact by excluding the self-citations. Self-citation is ethical when it is a necessity. Nevertheless, there is a threshold for self-citations. Thomson Reuters’ resource, known as the Web of Science (WoS) and currently lists journal impact factors, considers self-citation to be acceptable up to a rate of 20%; anything over that is considered suspect (10). In some journals, even 5% is considered to be a high rate of self-citations. The ‘Journal Citation Report’ is a reliable source for checking the acceptable level of self-citation in any field of study. The Public Policy Group of the London School of Economics (LSE) published a handbook for “Maximizing the Impacts of Your Research” and described self-citation rates across different groups of disciplines, indicating that they vary up to 40% (11). Unfortunately, there is no significant penalty for the most frequent self-citers, and the effect of self-citation remains positive even for very high rates of self-citation (5). However, WoS has dropped some journals from its database because of untrue trends in the citations (4). The same policy also should be applied for the most frequent self-citers. The ethics of publications should be adhered to by those who wish to conduct research and publish their findings.
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References 1. Jalalian M, Mahboobi H. New corruption detected: Bogus impact factors compiled by fake organizations. Electron Physician. 2013;5(3):685-6. http://www.ephysician.ir/2013/685-686.pdf 2. Ale Ebrahim N, Salehi H, Embi MA, Habibi Tanha F, Gholizadeh H, Motahar SM, et al. Effective Strategies for Increasing Citation Frequency. International Education Studies. 2013;6(11):93-9. doi: 10.5539/ies.v6n11p93, http://opendepot.org/1869/1/30366-105857-1-PB.pdf 3. Mahian O, Wongwises S. Is it Ethical for Journals to Request Self-citation? Sci Eng Ethics. 2014:1-3. doi: 10.1007/s11948-014-9540-1 4. Van Noorden R. Brazilian citation scheme outed. Nature. 2013;500:510–1. doi: 10.1038/500510a, http://boletim.sbq.org.br/anexos/Braziliancitationscheme.pdf 5. Fowler JH, Aksnes DW. Does self-citation pay? Scientometrics. 2007;72(3):427-37. doi: 10.1007/s11192- 007-1777-2 6. Schubert A. Using the h-index for assessing single publications. Scientometrics. 2009;78(3):559-65. doi: 10.1007/s11192-008-2208-3 7. Thor A, Bornmann L. The calculation of the single publication h index and related performance measures: a web application based on Google Scholar data. Online Inform Rev. 2011;35(2):291-300. 8. Thor A, Bornmann L. Web application to calculate the single publication h index (and further metrics) based on Google Scholar 2011 [cited 2014 3 May]. Available from: http://labs.dbs.uni-leipzig.de/gsh/ 9. Couto F. Citation Impact Discerning Self-citations 2013 [cited 2014 3 Ma]. Available from: http://cids.fc.ul.pt/cids_2_3/index.php 10. Epstein D. Impact factor manipulation. The Write Stuff. 2007;16(3):133-4. 11. Public Policy Group L. Maximizing the impacts of your research: a handbook for social scientists. London School of Economics and Political Science, London, UK.: 2011. http://www.lse.ac.uk/government/research/resgroups/lsepublicpolicy/docs/lse_impact_handbook_april_201 1.pdf
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Islamic World Science Citation Center (ISC), Malaysia Citation Center (MCC), Index Copernicus, EBSCO Host, Directory of Open Access Journals (DOAJ), Genamics Journal Seek , Open-J Gate, Iran Medex Directory of Biomedical Journals, J Gate, Ind Medica, Geneva Foundation for Medical Education and Research (GFMER), University of Chicago, Arkansas University, Johns Hopkins Libraries, University of Miami, University of Arkansas, University of Zurich, Oregon Health & Science University, Directory of Research Journals Indexing (DRJI), Utrecht University Library, John Brown University online library, University Library of Regensburg (Electronic Journals Library), Universidad Veracruzana, Library catalog of the University of Groningen, University library of Saskatchewan, and more. Asian Social Science; Vol. 9, No. 5; 2013 ISSN 1911-2017 E-ISSN 1911-2025 Published by Canadian Center of Science and Education Does Criticisms Overcome the Praises of Journal Impact Factor?
Masood Fooladi1, Hadi Salehi2, Melor Md Yunus3, Maryam Farhadi1, Arezoo Aghaei Chadegani1, Hadi Farhadi4 & Nader Ale Ebrahim5 1 Department of Accounting, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran 2 Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3 Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Malaysia 4 School of Psychology and Human Development, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia 5 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia Correspondence: Masood Fooladi, Department of Accounting, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran. E-mail: [email protected]
Received: February 16, 2013 Accepted: March 18, 2013 Online Published: April 27, 2013 doi:10.5539/ass.v9n5p176 URL: http://dx.doi.org/10.5539/ass.v9n5p176
Abstract Journal impact factor (IF) as a gauge of influence and impact of a particular journal comparing with other journals in the same area of research, reports the mean number of citations to the published articles in particular journal. Although, IF attracts more attention and being used more frequently than other measures, it has been subjected to criticisms, which overcome the advantages of IF. Critically, extensive use of IF may result in destroying editorial and researchers’ behaviour, which could compromise the quality of scientific articles. Therefore, it is the time of the timeliness and importance of a new invention of journal ranking techniques beyond the journal impact factor. Keywords: impact factor (IF), journal ranking, criticism, praise, scopus, web of science, self-citation 1. Introduction Citation is the reference text and recognition of article information. Generally, each article has a reference section at the end including all references cited in that article. Each reference is called a citation. The frequency of citations to an especial article by other articles means citation count. In addition, citation index as a type of bibliographic database traces the references in a published article. Citation index shows that how many times an article has been cited with other articles. In other words, one can find that which later article cites which earlier articles. Citations are applied to measure the importance of information contained in an article and the effect of a journal in related area of research activity and publications. The first citation index for published articles in scientific journals is Science Citation Index (SCI), which is founded by Eugene Garfield’s Institute for Scientific Information (ISI, previously known as Eugene Garfield Associates Inc.) in 1960 (Nigam & Nigam, 2012.). Then, it was developed to produce the Arts and Humanities Citation Index (AHCI) and the Social Sciences Citation Index (SSCI). The SSCI is one of the first databases developed on the Dialog system in 1972. Small (1973) develops his efforts on Co-Citation analysis as a self-organizing classification mechanism namely “Research Reviews”. Garner, Lunin, and Baker (1967) describes that the worldwide citation network has graphical nature. Giles, Bollacker, and Lawrence (1998) introduce the autonomous citation indexing, which can provide the automatic algorithmic extraction and classify the citations for any digital scientific and academic articles. Subsequently, several citation indexing services are created to automate citation indexing including Google Scholar, EBSCOhost, Institute for Scientific Information (ISI) and Elsevier (Scopus). Among all citation indexing services, ISI, has dominated the citation indexing career. ISI is a part of Thomson Reuters, which publishes the citation indexes in form of compact discs and print format. One can access to the products of ISI through the website with the name ‘Web of Science’ (WOS). It is possible to access seven databases in WOS including: Social Sciences Citation Index (SSCI), Index Chemicus, Science Citation Index
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(SCI), Arts & Humanities Citation Index (A&HCI), Current Chemical Reactions, Conference Proceedings Citation Index: Science and Conference Proceedings Citation Index: Social Science and Humanities. 2. Impact Factor One of the most important product of ISI is the journal impact factor (IF), which is first designed by Eugene Garfield. IF is published by ISI as a standardized measure to reveal the mean number of citations to the published articles in particular journal. Journal Citation Reports (JCR) defines the IF as the frequency of citation to the “average papers” published in a journal in an especial year. In effect, IF is almost used by research institutions, universities and governmental agencies to measure the influence and impact of a professional journal comparing with other journals in the same area of research (Kuo & Rupe, 2007). IF is also applied to evaluate the individual profession of scientists and researchers. Therefore, editors have a high tendency to increase the IF of their journals. In order to calculate the IF of a journal, the number of citations to the published papers in a journal, which is reported in the JCR year, is divided by the total number of papers published in that journal during two previous years. Citation may be comes from articles published in the same journal or different proceedings, journals or books indexed by WOS. As an example, an IF of 3.0 reveals that, on average, the papers published in a particular journal within two years ago, have been cited three times. Although the IF calculation for journals is based on articles published and cited in the previous two years, one can calculate the average citation using longer time periods. The 5-year journal IF is the number of citations to a particular journal divided by the total number of papers published in the journal since five years ago. In certain fields of study, the 5-year journal IF is more appropriate than 2-years because the body of citations in 5-years basis might be large enough to make a reasonable comparison. In some area of research, it takes a long time, more than two years, to publish a work and response to previous articles. The aggregate IF is applied for a subject category rather than a journal only (Khan & Hegde, 2009). The calculation is the same way as the IF for a journal, but it considers the total number of citations to all journals in a particular subject group and the number of papers published in these cited journals. The median IF refers to the median value of IF for all journals in the subject category. The number of citations to a journal divided by the total papers published in the journal in current year is known as immediacy index. The journal’s half-life in each year refers to the median age of the article cited in JCR. As an example, if the value of journal’s half-life equals to six in 2007, it means that the citations from 2002 until 2007 to the journal are 50% of all the citations to the journal in 2007. The journal ranking table displays the ranking of a journal in related subject category based on the journal IF (Khan & Hegde, 2009). 3. Praises for Impact Factor IF as a measure of journals’ influence has several advantages and benefits for researchers as well as librarians, knowledge managers and information professionals. Some of the advantages are listed below: 1) The primary advantage of impact factor is that it is very easy to measure. 2) Another advantage of IF is to mitigate the absolute citation frequencies. Since large journals provide a considerable body of citable literature which cites to the small journals, IF discounts the benefits of large journals in favour of small journals. In addition, there is a tendency to discount the benefits of high frequently published journals in favour of less frequently published journals and also matured journals over the newly established journals. In other words, journal IF is a remarkable measure for evaluating the journals since it offsets the benefits of age and size among the journals. 3) IF is a useful tool to compare different research groups and journals. It is commonly applied to administer scientific library collections. Librarians, knowledge managers and Information professionals encounter with limited budgets when they aim to select journals for their institutions and departments. ISI provide them with the IF as a measure to choose most frequently cited journals (Khan & Hegde, 2009). About 9000 social science and science journals from 60 countries are indexed by Web of Knowledge. 4) IF for indexed journals are greatly available and easy to understand and use. IF is more acceptable and popular than other alternative measures (Khan & Hegde, 2009). 4. Criticisms for Impact Factor However, IF has several inherent shortcomings overcoming the advantages of IF. The following problems refer to the calculation method of IF:
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1) It should be noted that Thomson Scientific is a commercial institution, which sells its products and evaluations to research institutions and publishers. Data and measures of IF are not publicly established and it is not easy for scientists to access these data and measures. Therefore, it is not subject to a peer review process. Researchers demand a fully transparency over how Thomson Scientific collect data and calculates citation metrics. Thomson Scientific is unable to clarify the used data to support its published IF and hence its IF is not reliable. Generally, findings of a scientific article could not be accepted by scientists when the primary data is not available. 2) The coverage of database is not complete since Thomson Scientific excludes some specific types of sources from the denominator. As an example, books (as a source for citations) are excluded from the database. Citations from journals which are not indexed in the ISI are not considered in IF calculation (Khan & Hegde, 2009). Falagas, Kouranos, Arencibia-Jorge, and Karageorgopoulos (2008) assert that one of the major shortcomings of journal IF is considering only “citable” papers mainly original papers and reviews. 3) IF could not be an appropriate measure because it is an arithmetic mean of number of citations to each paper and the arithmetic mean is not an appropriate measure (Joint Committee on Quantitative Assessment of Research 2008). IF does not consider the actual quality of research articles, their impressiveness, or the long-term impact of journals. It should be noted that IF and citation indexes indicate a specific, and relatively uncertain, type of performance. They do not provide a direct measure of quality (Moed, 2005). Therefore, Using the IF to derive the quality of individual articles or their authors seems to be idle (Baum, 2011). In addition, the validity and appropriate use of the IF as a measure of journal importance is a subject to controversy and another independent examination might reproduce a different IF (Rossner, Van Epps, & Hill, 2007). IF make more controversy when it is used to assess the articles published in the journals. IF appraises the reputation of the publishing journal not the quality of the content in the single papers. In addition, Falagas et al. (2008) assert that IF takes into account received citations only in a quantitative manner. It is conceivable that the published papers in a journal have a greater influence on science if these papers are cited by articles with a higher scientific quality. 4) The two-year or five-year window for measuring the IF might be logical for some fields of studies with a fast moving process of research, while it is not reasonable for some fields of study, which requires a long period for research or empirical validation and also takes a long time for review. These kinds of research might take a time longer than two years to be completed and then published. Therefore, citation to the original papers will not be considered in the IF of publishing journal. In addition, articles, which are published in many years ago and are still cited, have a significant impact on research area, but unfortunately, citation to these articles will not be considered due to their old age. 5) Distributing the same value of IF to each article published in a same journal leads to excessive variability in article citation, and provides the majority of journals and articles with the opportunity to free ride on a few number of highly cited journals and articles. Only some articles are cited anywhere near the mean journal IF. In effect, IF highly overstates the impact of most articles, while the impact of rest articles is greatly understated because of the few highly cited articles in the journal (Baum, 2011). Therefore, journal IF could not portray the individual impact of single articles and it is not much related to the citedness of individual articles in publishing journal (Yu & Wang, 2007). Seglan (1997) states that IF is wrongly used to estimate the importance of a single article based on where it is published. Since IF averages over all published papers, it underestimates the importance of the highly cited papers whereas overestimating the citations to the average publications. 6) Although, it is feasible to evaluate the IF of the journals in which an individual has published papers, the IF is not applicable to individual scientists or articles. ISI does not distribute a JCR for the humanities. The relevant number of citations received by a single paper is a better measure of importance and influence of individual papers as well as its authors (Khan & Hegde, 2009). Garfield (1998) caution regarding the “misuse in evaluating individuals” by IF due to a wide variation from paper to paper within an individual journal. 7) The IF is highly depended on different disciplines such as physical sciences and mathematical because of the speed with which articles are cited in a research area (Van Nierop, 2009). It is not logical to use IF as a measure to compare journals across different disciplines. The absolute value of IF does not make any sense. For example, a journal with the value of IF equal to two could not have much impact in fields and disciplines such as Microbiology, while it would be impressive in Oceanography. Therefore, IF as a measure of comparison between different fields and disciplines could be considered as invalid. In addition, this comparison has been widely made not only between the journals, but also between scientists or university departments, while, absolute IF could not estimate the scientific level of department. In an evaluation program, especially for doctoral degree granting institutions, reviewers consider the IF of journals in examining the scholarly outputs. It
178 www.ccsenet.org/ass Asian Social Science Vol. 9, No. 5; 2013 makes a bias in which some types of researches are undervalued and falsifies the total contribution of each scientist (Khan & Hegde, 2009). 8) IF is not relevant in some disciplines such as engineering, where the main scientific outputs are technical reports, conference proceedings and patents. Regarding the literature, IF is not a relevant measure because books constitute the most important publications in literature, which cite other books. 9) The number of citations from journals in less universal languages and less-developed countries is understated, since only ISI database are utilized to define the IF. It is argued that the simple methodology applied in the calculation of IF provides editors with the opportunity to use different practices in order to inflate the impact factor of their journals (Garfield, 1996; Hemmingsson, Skjennald, & Edgren, 2002; The PLoS Medicine Editors, 2006). Reviewers may request from the authors to expand the article’s citation before publishing the articles. This is usual in peer review process in order to improve the quality of article. On the other hand, IF indicates the importance of a journal in related subject category. Therefore, editors have a high tendency to increase their journal IF (Yu, Yang, & He, 2011). There are several methods for a journal to increase its IF. One policy is to manipulate the IF (Dong, Loh, & Mondry, 2005). References to a journal can be managed in order to increase the number of citations to a journal and hence raise the IF (Smith, 1997). Therefore, these journals seem to have additional impact and their quality and quantity will be increased. However, there is no actual improvement in the journal impact and influence. This manipulation of IF will only result in artificial evaluation. 1) Self-citation is a common way to manipulate and increase the IF of journals (Miguel & Marti-Bonmati, 2002; Fassoulaki, Papilas, Paraskeva, & Patris, 2002; Falagas & Kavvadia, 2006; Falagas & Alexiou, 2007; Yu et al., 2011). Editorial board may force authors to expand the article’s citations not necessarily to improve the quality of scientific articles but they aim to inflate the IF of their journal in order to artificially raising the journal's scientific reputation (Wilhite & Fong, 2012). This is a business policy, which is called coercive citation. In effect, editors require authors to cite papers published in the same journal (self-citation) before the journal accepts to publish the article even if these citations are not relevant to the research. In their study, (Wilhite & Fong, 2012) find that 20% of researchers in psychology, sociology, economics and multiple business disciplines has experienced coercive citation. They find that journals with a lower IF have a high tendency for coercive citation in order to inflate their IF. Another study documents that coercive citation is common in other scientific disciplines. 2) Another policy to manipulate the IF is to focus on publishing review articles rather than research articles because review articles have a large body of literature, which are highly citable (Andersen, Belmont, & Cho, 2006; Falagas et al., 2008; Arnold & Fowler, 2011). Therefore, review articles provide the higher IF for publishing journals and these journals will have the highest IF in their respective research area. Journals have a tendency to publish a large number of their articles, which are likely to receive a high citation in near future or deny accepting papers such as case report in medical journals, which are not expected to be cited. Considering the above drawbacks of IF, it is suggested that IF as a measure of journal evaluation should have certain characteristics (Offutt, 2008). It should be independent from the length and number of the papers published in the journal. In addition, it should consider essential advances in the area over short term or incremental contributions. IF should have relative stability in a year to year basis. The most important is that IF should evaluate the impact of a journal during a long period since some articles are still cited after 10, 20, or even 50 years from the date of publication. In addition, we should consider the warnings by Garfield against using to compare different IF and scientific field. Beside the IF we can consider other internet databases to calculate the IF of journals or articles which is collaborating with Elsevier. The journal IF is an indicator of journal performance, and as such many of the suggestions for performance indicators could equally be used to the application of journal IF (Greenwood, 2007). In November 2007 the European Association of Science Editors (EASE) published an official statement suggesting that journal IF can be applied only and cautiously for assessing and comparing the importance and impact of journals, but not for evaluating the single articles, and of course not for appraising the research programmes or researchers (EASE, 2007). The International Council for Science (ICSU) Committee on Freedom and Responsibility in the conduct of Science (CFRS) issued a "Statement on publication practices and indices and the role of peer review in research assessment" in July 2008, which suggest to consider only a limited number of articles published per year by each scientist or even give a penalty to scientists due to excessive number of publications in each year (Smith, 1997). The Deutsche Forschungsgemeinschaft (2010), German Foundation for Science, issued new strategies to assess only papers and no bibliometric information on candidates in decision making process
179 www.ccsenet.org/ass Asian Social Science Vol. 9, No. 5; 2013 regarding: "...performance-based funding allocations, postdoctoral qualifications, appointments, or reviewing funding proposals, [where] increasing importance has been given to numerical indicators such as the h-index and the impact factor". The citation style of manipulated journals, especially the number of self-citations in the last two year appears to be unusual. It can be seen that the self-cited rate of manipulated journals is higher than that of the normal journal. Therefore, self-citation rate can be used as a measure to specify the manipulated journals and calculate the real IF by deducting the self-citations from the reported IF. However, it is a difficult task to specify manually each journal in the JCR database. As discussed, journal might publish a larger number of review articles in order to inflate their IF. Therefore, the Thomson Scientific website can provide directions for removing review articles from the calculation (Khan & Hegde, 2009). 5. Conclusion Although IF is a popular measure of quality for journals, we conclude that journal IF has its own restrictions. We believe that there should be more examination of whether and how IF evaluates journal quality before it is broadly accepted as a measure of journal quality. The IF measurement would certainly never assess the peer review process of journals (Offutt 2008). It means that IF is not an appropriate measure to assess the quality of published articles. One important factor in assessing a progress case is to consider the number of published articles but it is an imperfect method because it considers the number of articles only instead of the quality of publications. However, it is very hard to assign professionals to read articles and give an independent opinion about the impact of particular scientist on his or her research area. The implication for researchers and journals is that they should not rely only on this indicator. If we do not consider the above limitations associated with IF, decisions made based on this measure is potentially misleading. Generally, a measure will fall into disuse and disrepute among the scientific community members, if it is found to be invalid, unreliable, or ill-conceived. Although IF has many limitations as discussed in this paper, it does not lose its reputation and application by the scientific society. Indeed, IF attracts more attention and being used more frequently by scientists and librarians, knowledge managers and information professionals. Critically, extensive use of IF may result in destroying editorial and researchers’ behaviour, which could compromise the quality of scientific articles. Calculation of IF and policies to increase the IF by journals, may push researchers to consider publication as a business rather than contribution to the area of research. It is not fare that we should rely on such a non-scientific method which IF appraises the quality of our efforts. It is the time of the timeliness and importance of a new invention of journal ranking techniques beyond the journal impact factor. There should be a new research trend with the aim of developing journal rankings that consider not only the raw number of citations received by published papers, but also the influence or importance of documents which issue these citations (Palacios-Huerta & Volij, 2004; Bollen, Rodriguez, & van de Sompel, 2006; Bergstrom, 2007; Ma, Guan, & Zhao, 2008). The new measure should represent scientific impact as a function not of just the quantity of citations received but of a combination of the quality and the quantity. References Andersen, J., Belmont, J., & Cho, C. T. (2006). Journal impact factor in the era of expanding literature. Journal of Microbiology, Immunology and Infection, 39, 436-443. Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the American Mathematical Society, 58(3), 434-437. Baum, J. A. C. (2011). Free-riding on power laws: Questioning the validity of the impact factor as a measure of research quality in organization studies. Organization, 18(4), 449-466. http://dx.doi.org/10.1177/1350508411403531 Bergstrom, C. (2007). Eigenfactor: Measuring the value and prestige of scholarly journals, College & Research Libraries News, 68(5), 314-316. Bollen, J., Rodriguez, M. A., & Sompel, H. (2006). Journal status, Scientometrics, 69(3), 669-687. http://dx.doi.org/10.1007/s11192-006-0176-z Dong, P., Loh, M., & Mondry, A. (2005). The impact factor revisited. Biomedical Digital Libraries, 2. European Association of Science Editors (EASE). (2010). Statement on inappropriate use of impact factors. Retrieved from http://www.ease.org.uk/publications/impact-factor-statement Falagas, M. E., & Alexiou, V. G. (2007). Editors may inappropriately influence authors’ decisions regarding
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Van Nierop, E. (2009). Why do statistics journals have low impact factors? Statistica Neerlandica, 63(1), 52-62. http://dx.doi.org/10.1111/j.1467-9574.2008.00408.x Wilhite, A. W., & Fong, E. A. (2012). Coercive citation in academic publishing. Science, 335(6068), 542-543. http://dx.doi.org/10.1126/science.1212540 Yu, G., & Wang, L. (2007). The self-cited rate of scientific journals and the manipulation of their impact factors. Scientometrics, 73(3), 356-366. http://dx.doi.org/10.1007/s11192-007-1779-8 Yu, G., Yang, D. H., & He, H. X. (2011). An automatic recognition method of journal impact factor manipulation. Journal of Information Science, 37(3), 235-245. http://dx.doi.org/10.1177/0165551511400954
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Australian Journal of Basic and Applied Sciences, 7(4): 198-202, 2013 ISSN 1991-8178
Does it Matter Which Citation Tool is Used to Compare the h-index of a Group of Highly Cited Researchers?
1Hadi Farhadi, 2Hadi Salehi, 3Melor Md Yunus, 4Arezoo Aghaei Chadegani, 4Maryam Farhadi, 4Masood Fooladi and 5Nader Ale Ebrahim
1School of Psychology and Human Development, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia 2Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Malaysia 4Department of Accounting, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran 5Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
Abstract: h-index retrieved by citation indexes (Scopus, Google scholar, and Web of Science) is used to measure the scientific performance and the research impact studies based on the number of publications and citations of a scientist. It also is easily available and may be used for performance measures of scientists, and for recruitment decisions. The aim of this study is to investigate the difference between the outputs and results from these three citation databases namely Scopus, Google Scholar, and Web of Science based upon the h-index of a group of highly cited researchers (Nobel Prize winner scientist). The purposive sampling method was adopted to collect the required data. The results showed that there is a significant difference in the h-index between three citation indexes of Scopus, Google scholar, and Web of Science; the Google scholar h-index was more than the h-index in two other databases. It was also concluded that there is a significant positive relationship between h- indices based on Google scholar and Scopus. The citation indexes of Scopus, Google scholar, and Web of Science may be useful for evaluating h-index of scientists but they have some limitations as well.
Key words: h-index, Scopus, Google Scholar, Web of Science, Nobel Prize, Physics, Chemistry, Economic Sciences.
INTRODUCTION
Nowadays, when citation information is needed there are a few sources to rely on. The most comprehensive web version of citation indexes sources are Scopus (http://www.scopus.com/), Google Scholar (http://scholar.google.com/), and the Web of Science (http://portal.isiknowledge.com/). Scopus, Google scholar, and Web of Science are regarded as the most useful and trustful source for searching. They are valuable tools for searching which provide the citation searching and ranking by times cited (Mikki, 2009). These three sources of citation indexes are multidisciplinary and international coverage and used to assess the scientific output worldwide (Bar‐Ilan, 2008).
Background: To quantify the research output of a single scientist, Hirsch (2005) introduced the h-index. h-index is calculated by the number of publications and the number of citations received. As defined by Hirsch (2005), a scientist has index h if h of his or her Np papers have at least h citations each and the other (Np-h) papers have ≤ h citations each. It is a single number which supports a good representation of the scientific lifetime achievement (Egghe & Rousseau, 2006; van Raan, 2006). For example, if a scientist has published 15 papers that each had at least 15 citations, h-index of him/her will be 15. Although this single number is simple to compute and it takes into account both the quantity and impact of the papers, it is essential to consider which database is reporting this single number (h-index). h-index is computed based upon the data from the aforementioned citation indexes to measure the scientific output of a researcher. The data from these citation indexes are important for scientists and universities in all over the world. For instance, h-index is one of the impressive and effective factors used by promotion committees to evaluate research productivity and impact at universities. Although, citing and cited documents from these citation tools are associated to each other, h-index can be obtained separately from different citation databases of Scopus, Google Scholar, and Web of Science. That is because each of these citation indexes has a
Corresponding Author: Hadi Farhadi (School of Psychology and Human Development, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia E-mail: [email protected] 198
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different collection policy which influences both the publications covered and the number of citations (Bar‐Ilan, 2008). Hence, the outputs and the results from these sources of citation indexes should not be approximately similar. This study aims to distinguish the major differences on h-index of scientists between three citation databases of Scopus, Google Scholar, and Web of Science. Therefore, based upon the literature, this study attempts to answer the following research questions: 1) How much the results from these sources of citation indexes are different? 2) Can universes and promotion committees use the h-index of a scientist as a tool for synthesizing and depicting the scientific production in a more exhaustive way? To answer the above research questions, the current study investigated the differences between the outputs and the results from these three citation databases (Scopus, Google Scholar, and Web of Science) based upon the h-index of a group of highly cited researchers (Nobel Prize winner scientists).
Methodology: There exist three commonly web versions of citation indexes sources to measure the h-index of a scientist. Hence, this study computed the h-index for 12 Nobel Prize winners based upon data from Scopus, Google Scholar, and Web of Science for three different fields of study namely physics, chemistry and economic sciences. Then, we compared the h-index data of these 12 Nobel Prize winners to find out the correlation between the h-index of three mentioned citation indexes sources. Purposive sampling method was used to collect the required data. To do so, we used the official web site of the Nobel Prize (http://www.nobelprize.org/) as our starting point, which lists the all Nobel Prize winners. Every year, the Nobel Foundation in Stockholm, Sweden, presents an international award called “Nobel Prize” to those who have made outstanding contributions to their disciplines. The Nobel Prize includes a medal, a diploma, and cash award given annually to those who during the preceding year have given the greatest benefit to human beings. Every year, the respective Nobel Committees invite thousands of university professors, members of academies and scientists from different countries, previous Nobel laureates, and members of parliamentary assemblies to submit candidates for the Nobel Prizes for the coming year. These nominators are chosen in such a way that as many countries and universities as possible are represented over time. For this study, we selected 12 scientists from the official website of the Nobel Prize who have won the Nobel Prize in three different fields of physics, chemistry, and economic sciences. Then the name of each scientist was searched in three citation indexes sources of Scopus, Google Scholar, and Web of Science (see Figures 1, 2 and 3). We brought Serge Haroche (the physics Nobel prize 2012 winner) as an example in following figures. As can be seen in the following figures, the h-index of Serge Haroche is 34, 35 and 21 based upon Scopus, Google Scholar, and Web of Science, respectively.
Fig. 1: Serge Haroche’s h-index by Scopus is 34.
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Fig. 2: Serge Haroche’s h-index by Google Scholar is 35.
Fig. 3: Serge Haroche’s h-index by Web of Science is 21.
RESULTS AND DISCUSSION
Table 1 shows the h-index of 12 Nobel Prize winner scientists in three different citation indexes of Scopus, Google Scholar and Web of Science.
Table 1: h-index of 12 Nobel Prize winner scientists in three different citation indexes. Noble Prize Winner’s Name Year Research field Web of Science Scopus Google Scholar Serge Haroche 2012 Physics 21 34 35 David J. Wineland 2012 Physics 20 47 23 Saul Perlmutter 2011 Physics 35 38 32 Brian P. Schmidt 2011 Physics 21 46 62 Robert J. Lefkowitz 2012 Chemistry 31 106 167 Brian K. Kobilka 2012 Chemistry 24 63 70 Dan Shechtman 2011 Chemistry 11 5 13 Akira Suzuki 2010 Chemistry 85 56 79 Alvin E. Roth 2012 Economic Sciences 4 28 68 Thomas J. Sargent 2011 Economic Sciences 11 21 77 Christopher A. Sims 2011 Economic Sciences 13 13 64 Peter A. Diamond 2010 Economic Sciences 6 14 61
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Our first step in understanding the differences between Scopus, Google Scholar, and Web of Science based upon the scientists’ h-index was to examine the means of each citation tool resource. Figure 4 shows the mean of h-index for Nobel Prize winner scientists based upon data from Google Scholar (mean=62.58), Scopus (39.25), and Web of Science (23.5). As can be seen in Figure 4, the mean of Google scholar h-index (mean = 62.58) for scientist in this research is more than the mean of Scopus h-index (mean = 39.25) and mean of Web of Science h-index (mean = 23.5). The results can be reasonable because Google Scholar covers more articles indexed than other two citation tools and it contains books and conference proceedings which may alter considerable citation metrics (Vanclay, 2007; Bar‐Ilan, 2008; Mikki, 2009).
Fig. 4: Comparing the h-index of 12 Nobel Prize winners based upon data from Scopus, Google Scholar and Web of Science.
Table 2 also shows the multiple regression between the h-index of 12 Nobel Prize winners based upon data from Scopus, Google Scholar and Web of Science. The multiple regression results show that there is a positive significant relationship between Scopus and Google Scholar h-index (β=.799, p<.05). This indicates that more h-index in Scopus citation tool corresponds with more Google Scholar h-index. In other words, we can conclude that the h-index of Scopus can predict the Google scholar h-index. However, this study could not find a significant relationship between the h-index in Web of Science and Google Scholar h-index.
Table 2: Multiple regression between the h-index of 12 Nobel Prize winners based upon data from Scopus, Google Scholar and Web of Science. B Beta t Constant 24.761 1.561 Web of Science -.300 -.164 -.639 Scopus 1.143 .799 3.122* R2= 0.54, F= 5.32, p<0.05
Based upon the samples’ field of studies, the results showed that the h-index mean of chemistry (mean = 59.17) is more than the h-index mean of physics (mean = 37.5) and economy (mean = 31.67). It means that the citation and publication in chemistry area are more than those of physics and economy (see Figure 5).
Fig. 5: Comparing the h-index of 12 Nobel Prize winners based upon their research area.
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Conclusions: h-index retrieved by citation indexes of Scopus, Google Scholar, and Web of Science is used to measure the scientific performance and the research impact studies based upon the number of publications and citations of a scientist. It also is easily available and may be used for performance measures of scientists and for recruitment decisions. As the results showed for the samples of this study, the difference in the h-index between three citation indexes of Scopus, Google Scholar and Web of Science is obvious and noticeable. The Google Scholar h-index is more in comparison with the h-index in two other databases. Moreover, the findings showed that there is a significant relationship between h-indices based upon Google Scholar and Scopus. We can conclude that it matters which citation tools are used to compute the h-index of scientists. Therefore, the universities and promotion committees need to consider which citation tool is used to compute the h-index of a researcher or a lecturer. In general, when we intend to report the h-index of a scientist, we need to consider and mention the sources of citation indexes. We cannot compare the h-index of two scientists without mentioning the sources of citation indexes. The citation indexes of Scopus, Google Scholar and Web of Science might be useful for evaluating the h-index of scientists but they have their own limitations as well.
REFERENCES
Bar‐Ilan, J., 2008. Informetrics at the beginning of the 21st century: A review. Journal of Informetrics, 2(1): 1‐52. Bar‐Ilan, J., 2008. Which h‐index? A comparison of WoS, Scopus and Google Scholar. Scientometrics, 74(2): 257‐271. Egghe, L., R. Rousseau, 2006. An informetric model for the Hirsch-index, Scientometrics, 69(1): 121-129. Google Scholar. (No Date). About Google Scholar. Retrieved 27 January 2013, from http://scholar.google.com/. Hirsch, J.E., 2005. An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46): 16569‐16572. ISI WoS. Thomson Scientific. (No Date). Web of Science. Retrieved 27 January 2013, from http:// http://portal.isiknowledge.com/. Mikki, S., 2009. Google Scholar Compared to Web of Science: A Literature Review. Nordic Journal of Information Literacy in Higher Education, 1(1): 41‐51. Nobel Prize. (No Date). About Nobel Prize. Retrieved 27 January 2013, from http://www.nobelprize.org/. Scopus. (No Date). About Scopus. Retrieved 27 January 2013, from http://www.scopus.com/. Van Raan, A.F.J., 2006. Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups. Scientometrics, 67(3): 491-502. Vanclay, J.K., 2007. On the robustness of the h‐index. Journal of the American Society for Information Science and Technology, 58(10): 1547‐1550.
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International Journal of Management Science and Business Administration
Volume 1, Issue 3, February 2015, Pages 6-15
Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Nader Ale Ebrahim1, H. Ebrahimian2, Maryam Mousavi3, Farzad Tahriri3 1Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia 2Institute of Mathematical Sciences, Faculty Science, University Malaya. 3Centre for Product Design and Manufacturing, Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia Corresponding author (e-mail):[email protected]
Abstract: Earlier publications have shown that the number of references as well as the number of received citations are field-dependent. Consequently, a long reference list may lead to more citations. The purpose of this article is to study the concrete relationship between number of references and citation counts. This article tries to find an answer for the concrete case of Malaysian highly cited papers and Malaysian review papers. Malaysian paper is a paper with at least one Malaysian affilation. A total of 2466 papers consisting of two sets, namely 1966 review papers and 500 highly-cited articles, are studied. The statistical analysis shows that an increase in the number of references leads to a slight increase in the number of citations. Yet, this increase is not statistically significant. Therefore, a researcher should not try to increase the number of received citations by artificially increasing the number of references.
Key words: H-index, Citation analysis, Bibliometrics, Impact factor, Performance evaluation,
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Relations between citations and references
1. Introduction
Researchers seeking citation tracking to find the most influential articles for a particular topic and to see how often their own published papers are cited (Bakkalbasi et al. 2006). On the other hand universities are looking for citations because of its influence in the university ranking (Ale Ebrahim et al. 2013, Ioannidis 2010, Bornmann, Leydesdorff, and Wang 2014). A citation count is the number of times a research work such as a journal article is cited by other works. The citation per paper meaningfully influence a number of metrics, including total citation counts, citation speed, the ratio of external to internal cites, diffusion scores and h-index (Carley, Porter, and Youtie 2013). Citation counts still commonly use for the measure of research papers quality and reputation (Abt and Garfield 2002). The number of citations that an article receives measured its impact on a specific field (Lai, Darius, and Lerut 2012). Citation analysis is one of the most important tools to evaluate research performance (Bornmann et al. 2012). Citation indicator is important for scientists and universities in all over the world (Farhadi, Salehi, Yunus, et al. 2013). In the early stage, the relationship between the number of references and the number of the paper citation was investigated in the 1965 (UZUN 2006, de Solla Price 1965). A long reference list at the end of a research paper may be the key to ensuring that it is well cited (Corbyn 2010, Ball 2008). Hence, citation counts are correlated with reference frequencies (Abt and Garfield 2002). Webster, Jonason, and Schember (2009) raised the question “Does the number of references an article contains predict its citation count?” and found that reference counts explained 19% of the variance in the citation counts. Lancho-Barrantes, Guerrero-Bote, and Moya-Anegón (2010) found that not only the number, but also the citation impact of the cited references correlated with the citation counts for a paper. The higher the impact of the cited references, the higher the later impact of the citing paper (Bornmann et al. 2012). Review articles are usually highly cited compare to other types of papers (Meho 2007).
Review papers represent the existing knowledge in a given field and more likely to be cited (Alimohammadi and Sajjadi 2009). Several bibliometric studies highlighted that citation counts are a function of many factors besides the scientific quality (Bornmann et al. 2012), length of paper (Abt and Garfield 2002), visibility (Ale Ebrahim et al. 2014), optimize scholarly literature for academic
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
search engines (Beel, Gipp, and Wilde 2010), add the name of study in the title of all publications (Sarli and Holmes 2011), publishing in a journal with higher impact factor (Vanclay 2013), internet usage (Farhadi, Salehi, Embi, et al. 2013), gross domestic product (GDP) (Gholizadeh et al. 2014), number of authors (Krause 2009), self-archiving (Gargouri et al. 2010), publish in an open access journal (Swan 2010), collaborate with international authors (Pislyakov and Shukshina 2012), write paper with a Nobel laureates (Ball 2011) and many other (Ale Ebrahim et al. 2013) including write a review paper (Vanclay 2013) and use more references (Corbyn 2010). In this study the relationship between number of references and citation counts is determined. Webster, Jonason, and Schember (2009) mentioned “On average, review articles actually showed less of the relationship than standard articles” (Corbyn 2010). So, in this research both review and standard articles (papers) were investigated. 2466 articles consist of 1966 Malaysian review and 500 highly cited papers were selected to examine the relationship between number of references and citation counts in the given article.
2.Materials and methods
All data were obtained through Web of Science online academic database provided by Thomson Scientific. This database included the necessary information to examine the relationship between reference and citation counts for every review and highly cited papers published in Malaysia since 1980 to October 2013. Science Citation Index Expanded, Social Sciences Citation Index and Arts & Humanities Citation Index, were searched for reviews and highly cited papers. For each paper, all Bibliometrics data, especially the number of references and the number of times the paper has been cited during the interval between the year of publication and the year 2013, have been collected.Two samples set were selected: 1- The sample number one consisted of 1966 review papers in all disciplines from Malaysia, according to the Web of Knowledge’s classification system. Citation statistics produced by shorter than three years’ time frame may not be sufficiently stable (Adams 2005, UZUN 2006). Because, papers appearing in the Web of Science databases over the last few years, have not had enough time to accumulate a stable number of citations (Webster, Jonason, and Schember 2009). Therefore, the time span limited from 1980 to November, 2010; yielding a subsample of 721 publications (37% of the original sample). Publications with zero citation
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
were removed. In order to select the highly cited paper a threshold 10 times cited per year is considered. The association between the number of references (independent variable) and time cited per year (dependent variable) of highly cited review papers investigated with linear and non- linear models. 2- The sample number two comprises 500 highly cited publications from Malaysia. According to the Web Of Science classification, the results are obtained based on the article type and exclude the review articles, editorial material, conference papers and book review.
3. Results and discussion
Two sets of data 1- 1966 review papers and 2- 500 high cited papers, were investigated separately. The results and discussions are coming as follows.
Outliers for sample one (1966 review papers) Due to the effect of the age of an article, the number of citations cannot be a reference of highly cited paper. Therefore, the citation per year selected as a reference for highly cited paper. Papers with 10 times cited per year is considered as highly cited paper. Figure 3-1 shows the number of times cited per year for 660 review papers. A threshold was visually determined on 50 times cited per year. Papers with more than 50 times cited yearly is called “extremely high cited paper” and detected as outliers. Papers with more than 300 listed references also detected as outliers (3-2).
Figure 3-1 Number of times cited per year vs number of review papers references
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Figure 3-2 Number of times cited per year vs number of references in review paper
Correlation analysis for sample one (1966 review papers) The correlation between variables was modeled with regression model, linear model y = α x + β and exponential model, non-linear model y = α eβx. The goodness of both model was ρ τ then measured with Spearman's rho ( ), Kendall's tau ( ) and Pearson correlation coefficient (r). The result of correlation analysis is summarized in 3-1.
Coefficient Statistic value P-value Equation Model ρ 0.127 0.193 y = 0.0639x + 10 τ 0.112 0.093 y = 10e0.0041x Linear r 0.152 0.120 Non-linear
Table 3-1 The result of correlation analysis of highly-cited review papers
The association between variables is graphically illustrated with scatter plots. The trend of these associations was drawn with solid lines. Refer to Figure 3 and Figure 4, both linear and non-linear models are not significantly fitted, trends are positive which support the hypothesis “For a given review paper, increasing in the number of references may have result of increasing the times cited per year”.
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Figure 2-3 Relationship between number of references and citation counts in review papers (linear model)
Figure 3-3 Relationship between number of references and citation counts in review papers (Exponential model)
Outlier detection for sample two (500 highly cited papers) Papers with 10 times cited per year is considered as highly cited paper. Papers that cited more than 100 times per year is considered as extremely high cited paper and detected as an outlier. Figure 5 and Figure 6 are showing raw data and filtered data respectively.
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Figure 3-4 Raw data - Number of times cited per year vs number of references 500 highly cited papers
Figure 3-5 Filtered data - Number of times cited per year vs number of references in 500 highly cited papers
Correlation analysis for sample two (500 highly cited papers) The association between the number of references (independent variable) and time cited per year (dependent variable) of first 500 high cited papers investigated with linear and non-linear model correlation analysis. The correlation was modeled with regression model, linear model y = α x + β and exponential model, non-linear model y = α eβx. The goodness of fit was then measured with
Spearman's rho (ρ) , Kendall's tau (τ ) and Pearson correlation coefficient (r) . The result of correlation analysis is summarized in Table 3-2.
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Coefficient Statistic value P-value Equation Model ρ 0.08 0.914 y = 0.03 x + 2.4 τ 0.04 0.467 y = 1.1 e0.08 x Linear r 0.06 0.419 Non-linear
Table 3-2 The result of correlation analysis of 500 highly cited papers
The association between variables is graphically illustrated with scatter plots. The trend of these associations is shown by the solid lines. Figure 3-7 and Figure 3-8 shows, although both linear and non-linear models are not significantly fitted, positive values of correlation coefficients are still suggesting a positive trend (positive correlation) on the number of references and the number of times cited per year.
Figure 3-6 Relationship between number of references and citation counts in 500 highly cited (linear model)
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Figure 3-7 Relationship between number of references and citation counts in 500 highly cited (Exponential Model)
4. Conclusion
This study shows that since the trend between the citation count and the number of references is not statistically significant, we cannot conclude that there is a significant association between the citation count of Malaysia review papers between the given period and number of references contained in the paper. The correlation coefficient is not statistically significant. However, r = 0.152 based on the population of 721 articles. Malaysian review papers get more citations than other types of papers. The number of references in the article has the lowest impact on the citation compares with review paper. As this study looked only Malaysia review papers and 500 highly-cited article, it would be necessary to conduct a similar study in the otherworld and types of papers. It would be important to examine whether in other types of papers the relationship investigated here have significant correlated or not. The research considered the general definition of citations. Therefore, future studies may make a diffrentianain between “perfunctory citations” and “organic citations” citations as Tang and Safer (2008) defined “perfunctory citations” is occurred only once and in the introduction, “organic citations” as references cited for “conceptual ideas” and “methodology and data” reasons.
ACKNOWLEDGEMENT
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
Sincere thanks to Dr. Bojan Obrenović and the International Journal of Management Science and Business Administration’s board members for their useful advices.
References
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Nader Ale Ebrahim, H. Ebrahimian, Maryam Mousavi, Farzad Tahriri Does a Long Reference List Guarantee More Citations? Analysis of Malaysian Highly Cited and Review Papers
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Modern Applied Science; Vol. 8, No. 5; 2014 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Economic Growth and Internet Usage Impact on Publication Productivity among ASEAN’s and World’s Best Universities
Hossein Gholizadeh1, Hadi Salehi2, Mohamed Amin Embi3, Mahmoud Danaee4, Ali Ordi5, Farid Habibi Tanha6, Nader Ale Ebrahim7 & Noor Azuan Abu Osman1 1 Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia 2 Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3 Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia 4 Faculty of Agriculture, Roudehen Branch, Islamic Azad University, Roudehen, Iran 5 Universiti Teknologi Malaysia, Advance Informatics School (AIS), Kuala Lumpur, Malaysia 6 Department of Financial Sciences, University of Economic Sciences, Tehran, 1593656311, Iran 7 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia Correspondence: Hadi Salehi, Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran. E-mail: [email protected]
Received: July 3, 2014 Accepted: July 21, 2014 Online Published: September 1, 2014 doi:10.5539/mas.v8n5p169 URL: http://dx.doi.org/10.5539/mas.v8n5p169
Abstract Measuring the number of papers which are published each year, publication productivity is the factor which shows the reputation of universities and countries. However, the effect of growing economy and using internet on the publication productivity in Asian countries has not been discovered yet. The present research is going to figure out the publication productivity among the elite universities in Asian countries and also ten top universities around the world in the last twenty years (from 1993 to 2012). Furthermore, the current research is aimed to study the relationship among publication, gross domestic product (GDP) and internet usage. It is worth to mention that the publication of the top Ten Malaysian Universities was regarded for the similar period of time. To get the exact numbers of documents like papers, conference articles, review papers and letters which are published by the universities in the last twenty years, the writer of the same paper used the Science Direct database. Moreover, the data for GDP and the number of internet usage was collected through the World Bank database (World Data Bank).To compare all kinds of publications,one-way ANOVAwas used and to investigate the impact of economic growth and internet usageon publication productivity, multiple regression analysis was applied.The results showed that therate of publication growth was 1.9, 20.9, and 65.5 % in top universities in the world, ASEAN countries and Malaysia, respectively.The results also showed thatthere was a positive and significant correlationbetween GDP and the number of internet users with the number of publications in ASEAN and Malaysian universities. Internet usagehad much more influence in comparison withthe GDP in predicting the number of publicationsamong these groups except for top ten Malaysian universities from 2003 to 2012. In summary, publication trends in top ten Malaysian and ASEAN universities are promising. However, policy makers and science managersshouldspend much more percentage of their GDP on Internet facilities and research studies that their outputs lead to more rapid economic growthand internet usage. Keywords: ASEAN, publication productivity, documents, internet usage, GDP, Malaysian Universities, publication trend 1. Introduction Ten countries in Southeast Asia formed the geo-political and economic Association of Southeast Asian Nations. It was first formed on 8 August 1967 by Indonesia, Malaysia, the Philippines, Singapore and Thailand. Some other countries like Brunei, Burma (Myanmar), Cambodia, Laos, and Vietnam became the members of this group and expanded it. The chief purpose of organizing this group is increasing economic growth (Sarel, 1997).
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Gross domestic product (GDP) which refers to the market price of all officially realized goods and services which are produced in a country in a specific period of time is the chief tool to measure the economy of a country. Reviewing the previously published papers, the writer found out that there is a relationship between economic growth with education and paper publication. The above mentioned result is not fully support by realistic facts (Jin, 2013; Nelson, 1966; Lucas Jr, 1988; Becker, 2009; Romer, 1990). Positive and significant impact of education on economic growth has been found by Mankiw et al. (1992) and Barro (1991) (Mankiw et al., 1992 & Barro, 1991); however, a possibility of reverse relationship between economic growth and education was shown by Bils and Klenow (2000). Besides, Jin and Jin newly indicated that the effect of publication productivity on economic growth is not the same in different fields. For example, there is a positive relationship between engineering and science with economic growth, while the social sciences do not have the same effect on economic growth (Jin, 2013). Nowadays, compared with the past twenty years, studying the publication productivity is a main topic for the researchers and students because the findings of the researches can positively affect the whole community Zain et al., 2009). According to the recent development rules, the numbers of educated employees have been enhanced. This matter helps the economy of the countries to grow very fast (Jin, 2013). It has been found out that those countries which are highly developed like the United Stated and England are among the world’s top productive research universities. The number of publication shows the research productivity and is employed to grade the countries and universities (Yazit and Zainab, 2007, Narin and Hamilton, 1996, Toutkoushian et al., 2003, Liu and Cheng, 2005, Meho and Spurgin). It can also be used to determine author’s productivity or the publication productivity of research groups (Liu, 2005; Hart, 2000; Uzun, 2002; Gu, 2001; Fox, 1983). Numerously referring to the previously published papers by the new papers a lot, shows the following identification and also the effect in the field of study. Those review articles which refer to other articles a lot, can give us some facts about the major areas of discipline besides, they can emphasize the increase of specific fields. Moreover, more frequently cited papers are mostly written by famous researchers who can impress future directions of the field by their ideas (Lefaivre and O’Brien, 2011, Kelly et al., 2010, Ponce and Lozano, 2010, Joynt and Leonard, 1980). Several indicators of academic or research performance are used to rank educational institutes and universities. They includealumni and staff winning Nobel Prizes and Fields Medals, highly cited researchers, papers published in Nature and Science, papers indexed in major citation indices, and the per capita academic performance of an institution. The Academic Ranking of World Universities (ARWU) is the first global ranking of universities to be published. Today, ARWU is regarded to be one of the three most influential and widely observed international university rankings, along with the QS World University Rankings and the Times Higher Education World University Rankings. The Academic Ranking of World Universities (ARWU), commonly known as the Shanghai Ranking, is a publication that was founded and compiled by the Shanghai Jiao Tong University to rank universities globally. The rankings have been conducted since 2003 and updated annually. The current study is mainly going to investigate the amount of publication productivity among the best universities in ASEAN countries and world’s top ten universities from 1993 to 2002 and 2003 to 2012.The study also aimed to achieve the following objectives: Studying the relationship among publication productivity, gross domestic product (current US $) and internet users Examining the publication direction of ten elite Malaysian Universities in a specific time periods Since the Science Direct offers about 20% more inclusion than Web of Science, it has been used as the first full-text theoretical database in this research. The researchers think that there is a positive relationship among the economic growth, the numbers of people who can use the internet and also the number of publication of papers in elite Asian universities and also the ten best universities in the whole world. 2. Methodology ScienceDirect database was used to collect the number of documentsincluding articles, conference papers, review papers, letters and books published in the last two decades from 1993 to 2002 and 2003to 2012. These data were collectedto make a comparison among the top university in each ASEAN country, top tenuniversities in Malaysia andtop tenuniversities in the world. To find the first university in each ASEAN countryand top tenMalaysian universities, we used the number of publications in ScienceDirect database. Moreover,to determine the top tenuniversities in the world, the Academic Ranking of World Universities (ARWU) was used. Furthermore,in each university,the main subject area (overall), number of papers published in Nature and Science journals, and the most cited paperswere identified.The numbers of citations that eachpaper could receive
170 www.ccsenet.org/mas Modern Applied Science Vol. 8, No. 5; 2014 during one week were identified as well. To compare all kinds of publicationsamongtop ten universities in the world, ASEAN and Malaysia), one-way ANOVA was applied. As the homogeneity test was not met, the Welch statistic was used to test the equality of means. Moreover, to evaluate the relationship between publications (articles, conference papers, review papers, letters) with GDP and Internet usage, Spearman correlation coefficient test was applied.To investigate economic growth and internet use impact on publication productivity,multiple regression was applied to examine what extent the proposed multiple liner regression model is supported by the research data. The regression examined how well the number of publications could be predicted from GDP and internet usage.In the multiple regression model, GDP and Internet usagewere set as the independent variables and the number of publications was considered as the dependent variable. 3. Results According to the Academic Ranking of World Universities (ARWU), the top tenuniversities in the world are mostly located in the United States (8 universities) or United Kingdom (2 universities) (see Table 1). Moreover, the first university in each ASEAN countryand top tenuniversities in Malaysia based on the number of publications in ScienceDirect database were listed in Table 1.The main research areas in world’s best universities were physics andastronomy (7 universities), medicine (2 universities) and engineering (1 university). In these universities, the average number of papers published in Nature and Sciencewere 1586 and 1419, respectively (see Table 1).
Table 1. The average number of published papers
most most Main most cited cited cited established Overall Main Subject papers papers paper Institution (country) paper paper (year) publication* Subject area area (% (Nature) (Science) (publication (citation) (citation) publication) date) 22/7/2013 29/7/2013 Harvard University Physics and 1636 74433 14.8 1430 2294 10224 10255 1990 (US) Astronomy Stanford University 1891 110914 Engineering 13.6 861 1593 6249 6266 2001 (US) Massachusetts Institute of Physics and 1861 134794 20.1 1563 1860 11678 11732 2000 Technology (MIT) Astronomy (US) University of Physics and California, Berkeley 1868 158231 15.3 1864 2233 18659 18757 1965 Astronomy (US) University of Physics and 1209 135913 14.9 4099 644 7966 7977 1990 Cambridge (UK) Astronomy California Institute Physics and
Top world's universities Top 1891 62675 26.3 974 1134 8657 8705 1995 of Technology (US) Astronomy Princeton University Physics and 1764 62273 20.1 754 945 6123 6136 1998 (US) Astronomy Columbia 1754 112569 Medicine 17.9 676 1403 10425 10484 1998 University (US) University of 1890 90126 Medicine 21.7 980 1560 11741 11777 1953 Chicago (US) University of Physics and 1096 122553 15.2 2658 526 10198 10216 2001 Oxford (UK) Astronomy National University of Singapore 1905 74484 Engineering 17.9 71 0 2171 2180 2003 (Singapore) University of 1949 21563 Medicine 14.9 24 0 445 449 2000 Universities Universities Top ASEAN ASEAN Top Malaya (Malaysia)
Mahidol University 1943 20291 Medicine 41.9 0 0 1494 1503 2005
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(Thailand)
Institut Teknologi Bandung 1959 2979 Engineering 23.6 0 5 330 330 2001 (Indonesia) International Rice Agricultural Research Institute 1960 2955 and 54 15 11 857 861 1972 (Philippines) Biological Vietnam National Computer University 1945 1230 15.2 0 0 153 153 2000 Science (Vietnam) UNIVERSITI Agricultural BRUNEI 1985 864 and 10.4 0 0 222 224 2003 DARUSSALAM Biological (Brunei) Institut Pasteur du Cambodge 1953 251 Medicine 44.9 0 0 680 687 2009 (Cambodia) Agricultural National University 1996 178 and 26.3 0 0 171 171 2003 of Laos (Laos) Biological University of 1878 109 Chemistry 13.6 0 1 77 77 1999 Yangon (Myanmar) University of 1949 21572 Medicine 14.9 24 0 449 2004 Malaya 452 Universiti Sains Material 1962 17054 13.3 0 0 808 1996 Malaysia Science 811 Agricultural Universiti Putra 1969 16322 and 15.7 0 0 453 2008 Malaysia Biological 455 Universiti Kebangsaan 1970 15010 Engineering 15.5 0 0 449 2004 Malaysia 452 UniversitiTeknologi 1975 10134 Engineering 26.1 0 0 260 2004 Malaysia 261 UniversitiTeknologi 1956-1965 6784 Engineering 22.5 0 0 305 2010 MARA 307 Top MalaysianUniversities Top International Islamic 1983 3995 Engineering 19.8 0 0 100 2007 University Malaysia 101 Multimedia 1996 3872 Engineering 27 0 0 275 2001 University 276 UniversitiTeknologi Computer 1997 3343 23.5 0 0 77 2008 Petronas science 77 Universiti Malaysia 2001 2321 Engineering 32.6 0 0 137 2007 Perlis 137
However, top universities in ASEAN countries could averagely publish 11 papers in Nature Journal and 2 papers in Science journal. The average numbers of citations for the most cited papers in each university in these three groups (world, ASEAN and Malaysia) were 10192, 660 and 331, respectively. Furthermore, the resultsshowed 39 citations per week for most cited papers in world's top universities while it was 4 citations per weekin ASEAN universities (see Table 1, Figure 1).
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Figure 1. Number of citations in one week for the most cited papers in each university (Top); Publication trend (Down)
University of Singapore with 74484 papers was the first in ASEAN countries and was followed by University of Malaya with 21563 publications among ASEAN countries. Moreover, the University of Yangon in Myanmar had the leastpublications (109) among ASEAN countries.Interestingly, publication trends in ASEAN countries are promising compared to the top ten universities in the world (see Appendixes 1 and 2).The rate of publication growth between these twodecades was 1.9, 20.9 and 65.5 percentagein top universities in the world, ASEAN and Malaysia, respectively (see Figure 1). To compare all kinds of publications, the results of ANOVA showed that there were significant differences among these three groups. Duncan’s multiple rangetest showed that there wasno significant difference between ASEAN and Malaysia universities, while both of them were significantly lower than top ten world universities (see Table 2). Table 3 shows the correlation between indices (articles, conference papers, review papers, and letters), GDP and internet users for these three groups. The results showedthat there was a positive and significant correlationbetween GDP and the number of internet users with the number of publicationsin ASEAN and top ten Malaysian universities. However, there was a negative correlationbetween GDP and internet users with the number of letters published from 1993 to 2002 in Malaysia. Moreover, there was a negative and significant correlationbetween GDP and the number of articles published in world's top universities (see Table 3). TheR-squared (R2) value presented in Table 4showed some information about the goodness of fit of a model. In regression, the R2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points. TheR2 of 1 indicates that the regression line perfectly fits the observeddata.
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TheR2value of 0.584 for ASEAN universities from 1993 to 2002 implies that the two-predictor model explained about 58.4% of the variance in publications. Table 5 revealed that based on the reported value of the F-statistic, the model fits the data. This means that the slope of the estimated linear regression model line was not equal to zero; thus, it confirmedthat there was a linear relationship between publication and the two-predictor variables.
Table 2. A (Welch robust Tests of Equality of Means); B (Pairwise comparison using Tamhane test) (A)
1993-2002 2003-2012
Welch statistic df Sig. Welch Statistic df Sig. Article 306.521 137.71 P<0.05 222.531 182.888 P<0.05 Conference 119.638 135.955 P<0.05 77.143 188.254 P<0.05 Review 159.91 142.948 P<0.05 221.178 160.595 P<0.05 Letter 143.944 168.747 P<0.05 101.268 156.708 P<0.05 Total publication 319.809 137.21 P<0.05 230.36 180.99 P<0.05
(B)*
Total Decade Article Conference Review Letter publication Top world 2030.73a 264.1a 124.6a 23.93a 2443.36a
2 ASEAN 222.98b 28.76b 6.55b 1.65b 259.94b
1993-200 Malaysia 66.58c 8.87b 2.13b 0.78b 78.36c Top world 3304.72a 776.77a 374.79a 58.3a 4514.58a
2 ASEAN 574.85b 166.54b 35b 7.04b 783.43b
2003-201 Malaysia 509.03b 235.81b 22.39b 3.31b 770.54b Means with same letter are not significantly different in each column. *data are log transformed.
Table 3. Correlation coefficient between the number of articles, conference papers, review papers and letters with GDP and internet usage for Top ten universities of the world, ASEAN and Malaysia
1993-2002 2003-2012
GDP internet GDP internet
Article -0.042 .229* -0.004 .531** Conference 0.178 .232* 0.012 0.158 WOELD Review 0.185 .497** -.443** -0.062 Letter -0.128 0.137 -.256* .324**
Article .723** .611** .779** .800** Conference .775** .599** .737** .739** ASEAN Review .565** .574** .518** .705** Letter .416** .416** .415** .567**
Article 0.133 .280** .624** .595** Conference .249* .400** .879** .876** MALAYSIA Review 0.09 0.171 .530** .442** Letter -0.03 -0.008 .338** .258* **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).
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Table 4. Results of multiple linear regression between GDP and internet usage with total number of publications for Top ten universities of the world, ASEAN and Malaysia, respectively 1993-2002
Unstandardized Coefficients Standardized Coefficients t Sig. R2 group B Std. Error Beta
Top world GDP* -1149.03 279.863 -0.408 -4.106 P<0.05 0.187 internet 21.03 4.943 0.423 4.254 P<0.05 ASEAN GDP* 197.843 70.785 0.214 2.795 P<0.05 0.584 internet 43.194 4.661 0.708 9.267 P<0.05
Malaysia GDP* 100.412 164.662 0.066 0.61 0.543 0.063 internet 1.771 0.888 0.216 1.994 P<0.05 * log transformed.
2003-2012
Unstandardized Coefficients Standardized Coefficients t Sig. R2 group B Std. Error Beta
Top world GDP* -1461.77 446.388 -0.297 -3.275 P<0.05 0.276 internet 121.544 25.935 0.425 4.687 P<0.05 ASEAN GDP* 363.724 161.614 0.177 2.251 P<0.05 0.551 internet 42.706 4.983 0.676 8.57 P<0.05 Malaysia GDP* 6219.755 1339.622 1.141 4.643 P<0.05 0.421 internet -50.131 23.01 -0.535 -2.179 0.102 * log transformed.
Standardized regression coefficients are presented in Table 4 to explain the importance of two predictors in predicting the number of publications. Independent variable with a high beta coefficient is highly important in contributing to the prediction of the number of total publications. Based on the beta values obtained, the beta coefficient in theworld’s top university was -0.408 and -0.297 for GDP and 0.423 and 0.425 for Internet usagebetween 1993-2002 and 2003-2012, respectively. This means that Internet usagehad a much more significant influence than the GDP in predicting the number of publications. The results showedthe same issuefor ASEAN universities meaning thatin these two decades, Internet usagehad much more significant effects than the GDP in predicting the number of publications. Interestingly, GDP had more power (1.141) than the number of internetusers (-0.535) from 2003 to 2012 in predicting the number of publications in top ten Malaysian universities. 4. Discussion and Conclusion In this study, a comparisonwas made among the top university in each ASEAN country, top ten Malaysian universities and the world’s top universitiesregarding the relationship between economic growth and internet usagewithpublication productivity from 1993 to 2002 and 2003 to 2012. Shanghai Ranking (ARWU) was used to determine the top ten universities in the world as it is the first global ranking of universities to be publishedand one of the three most influential and widely observed international university rankings. Moreover, the numbers of publications were used to find the first university in each ASEAN country and top ten Malaysian universities because most of these universities were not listed in the ARWU, QS, or the Times Higher Education World
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University Rankings. Publication productivity is an indicator of research output and could be used to rank countries and universities (Yazit and Zainab, 2007, Narin and Hamilton, 1996, Toutkoushian et al., 2003, Liu and Cheng, 2005, Meho and Spurgin). It can also be used to determine author’s productivity or the publication productivity of research groups and to assess the productivity of persons in a particular discipline (Liu and Cheng, 2005, Hart, 2000, Uzun, 2002, Gu and Zainab, 2001, Fox, 1983, Yi et al., 2008). World's best universities are mostly working on physics andastronomy. Institutes whichare working in these fields could publish more papers and get more citations. They could publish 144.2% and 709.5% more papers comparedto ASEAN universities in Nature and Science journals, respectively (see Table 1). They could alsoreceive 9.8 times more citations per week for their most cited papers (see Table 1).
Table 5. ANOVA table 1993
ANOVAb group Model Sum of Squares df Mean Square F Sig. world 1 Regression 1.756E7 2 8781193.216 12.390 .000a
Residual 6.875E7 97 708732.439
Total 8.631E7 99 asean 1 Regression 1.656E7 2 8279590.609 52.212 .000a
Residual 1.126E7 71 158575.023
Total 2.782E7 73 my 1 Regression 57700.227 2 28850.113 3.281 .042a
Residual 853014.813 97 8793.967
Total 910715.040 99 a. Predictors: (Constant), internet, log GDP. b. Dependent Variable: total publication.
2003
ANOVAb group Model Sum of Squares df Mean Square F Sig.
world 1 Regression 5.958E7 2 2.979E7 17.996 .000a
Residual 1.440E8 87 1655372.703
Total 2.036E8 89
asean 1 Regression 9.489E7 2 4.745E7 50.153 .000a
Residual 7.379E7 78 946035.347
Total 1.687E8 80
my 1 Regression 2.040E7 2 1.020E7 33.387 .000a
Residual 2.658E7 87 305522.329
Total 4.698E7 89 a. Predictors: (Constant), internet, log GDP. b. Dependent Variable: total publication.
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Publication trendfrom 1993 to 2012 in ASEAN and top ten Malaysian universitieswith 20.9 and 65.5% growth ispromising compared to the world’s top ten universities (1.9%). ASEAN and Malaysian universities havebeen averagely established over the past 66 and 37 years ago, respectively; while the top universities in the world have been averagely stabilised 327 years ago. This can be one of the reasons of high trend in publication productivity in ASEAN and Malaysian universities. In addition, the growth of publications might be overestimated due to the omitted variables, and a reverse causality from GDP to publications will be another possibility to occur.The number of publications in Malaysia increased dramatically after 2007 to 2012 (almost 5 times) (see Appendixes 1 and 2). One of the main reasons for increasing the number of paper publications in Malaysia could be greatly concentrating on enhancing the quality of research in the universities which are specific for researching like University of Malaya. Specified in the 10th Malaysia Plan, 1% of Malaysia GDP will be spent on the development and research projects. Furthermore, the other important reason could be the change of dissertations direction from conventional into research-based. PhD students have enhanced 10 times in the past years (there were about 4000 students in 2002, while they have increased to 40,000 in 2012). The top ten universities in Malaysia are shown in Table 1. It should be mentioned that the first five universities shown are ranked as the research universities and get more government funding. Thefindings of this study showed a positive and significant correlationbetween GDP and the number of publications in ASEAN and Malaysian universities. This finding was in line withthe findings of previous research studies that showedpositive and significant correlationbetween education and economic growth (Mankiw et al., 1992; Barro, 1991; Bils and Klenow, 2000). Interestingly, there is a negative and significant correlation between the number of publications (articles and letters) and economic growth in top ten Malaysian universities from 1993 to 2012. Besides, Jin and Jin showed in their recent study that the publication productivity affect the economic growth differently in various fields.In top ten universities located in the United States and United Kingdom, GDP could not have positive effects to increase the number of publications. While the significant effects of GDP on the number of publications were seen in Malaysian universities especially from 2003 to 2012. However, it should be noted that only the effects of GDP and internet usageon publications of recent years were evaluated in this study. Since the educational effects are accomplished over longer horizons, further investigation of the educational effects using the data that correspond to the stock of publications in earlier years would be important. Science managers and policy makers in ASEAN countries shouldspend much more percentage of their GDP on development and research projects as Malaysia does.Moreover, increasing the number of PhD students and changing the universityprograms to paper-based dissertations could be another solution to increase the number of publications. Furthermore, it is necessary to find out different ways to improve the quality and visibility of theresearch studiesand invest more on research studies that their outputs lead to more rapid economic growth. References Barro, R. J. (1991). Economic growth in a cross section of countries. The Quarterly Journal of Economics, 106, 407-443. http://dx.doi.org/10.2307/2937943 Becker, G. S. (2009). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press. Bils, M., & Klenow, P. J. (2000). Does schooling cause growth? American Economic Review, 1160-1183. http://dx.doi.org/10.1257/aer.90.5.1160 Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2008). Comparison of PubMed, Scopus, web of science, and Google scholar: Strengths and weaknesses. The FASEB Journal, 22, 338-342. http://dx.doi.org/10.1096/fj.07-9492LSF Fox, M. F. (1983). Publication productivity among scientists: A critical review. Social Studies of Science, 13, 285-305. http://dx.doi.org/10.1177/030631283013002005 Gu, Y., & Zainab, A. (2001). Publication productivity of Malaysian researchers in the field of Computer Science and Information Technology. Malaysian Journal of Library & Information Science, 6, 1-23. Hart, R. L. (2000). Co-authorship in the academic library literature: A survey of attitudes and behaviors. The Journal of Academic Librarianship, 26, 339-345. http://dx.doi.org/10.1016/S0099-1333(00)00140-3 Jin, J. C., & Jin, L. (2013). Research publications and economic growth: Evidence from cross-country regressions. Applied Economics, 45, 983-990. http://dx.doi.org/10.1080/00036846.2011.613785
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Joynt, R. L., & Leonard, J. A. (1980). Dantrolene sodium suspension in treatment of spastic cerebral palsy. Developmental Medicine & Child Neurology, 22, 755-767. http://dx.doi.org/10.1111/j.1469-8749.1980.tb03742.x Kelly, J., Glynn, R., O’briain, D., Felle, P., & Mccabe, J. (2010). The 100 classic papers of orthopaedic surgery a bibliometric analysis. Journal of Bone & Joint Surgery, British Volume, 92, 1338-1343. http://dx.doi.org/10.1302/0301-620X.92B10.24867 Lefaivre, K. A., & O’brien, P. J. (2011). 100 most cited articles in orthopaedic surgery. Clinical Orthopaedics and Related Research®, 469, 1487-1497. http://dx.doi.org/10.1007/s11999-010-1604-1 Liu, N. C., & Cheng, Y. (2005). The academic ranking of world universities. Higher education in Europe, 30, 127-136. http://dx.doi.org/10.1080/03797720500260116 Lucas, Jr, R. E. (1988). On the mechanics of economic development. Journal of monetary economics, 22, 3-42. http://dx.doi.org/10.1016/0304-3932(88)90168-7 Mankiw, N. G., Romer, D., & Weil, D. N. (1992). A contribution to the empirics of economic growth. The Quarterly Journal of Economics, 107, 407-437. http://dx.doi.org/10.2307/2118477 Meho, L. I., & Spurgin, K. M. (2005). Ranking the research productivity of lis faculty and schools: An evaluation of data sources and research methods. Journal of the American Society for Information Science and Technology, 56(12), 1314–1331. Narin, F., & Hamilton, K. S. (1996). Bibliometric performance measures. Scientometrics, 36, 293-310. http://dx.doi.org/10.1007/BF02129596 Nelson, R. R., & Phelps, E. S. (1966). Investment in humans, technological diffusion, and economic growth. The American Economic Review, 56, 69-75. Ponce, F. A., & Lozano, A. M. (2010). Highly cited works in neurosurgery. Part I: the 100 top-cited papers in neurosurgical journals: A review. Journal of neurosurgery, 112, 223-232. http://dx.doi.org/10.3171/2009.12.JNS091599 Romer, P. M. (1990). Endogenous technological change. Journal of political Economy, S71-S102. http://dx.doi.org/10.1086/261725 Sarel, M. (1997). Growth and productivity in ASEAN countries. International Monetary Fund. Toutkoushian, R. K., Porter, S. R., Danielson, C., & Hollis, P. R. (2003). Using publications counts to measure an institution's research productivity. Research in Higher Education, 44, 121-148. http://dx.doi.org/10.1023/A:1022070227966 Uzun, A. (2002). Productivity ratings of institutions based on publication in Scientometrics, Informetrics, and Bibliometrics, 1981–2000. Scientometrics, 53, 297-307. http://dx.doi.org/10.1023/A:1014864827441 Yazit, N., & Zainab, A. (2007). Publication productivity of Malaysian authors and institutions in LIS. Malaysian Journal of Library and Information Science, 12. Yi, H., Ao, X., & Ho, Y. S. (2008). Use of citation per publication as an indicator to evaluate pentachlorophenol research. Scientometrics, 75, 67-80. http://dx.doi.org/10.1007/s11192-007-1849-y Zain, Z. M., Ishak, R., & Ghani, E. K. (2009). The influence of corporate culture on organisational commitment: A study on a Malaysian listed company. European Journal of Economics, Finance and Administrative Sciences, 17, 16-26.
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International Education Studies; Vol. 6, No. 11; 2013 ISSN 1913-9020 E-ISSN 1913-9039 Published by Canadian Center of Science and Education Effective Strategies for Increasing Citation Frequency
Nader Ale Ebrahim1, Hadi Salehi2, Mohamed Amin Embi3, Farid Habibi Tanha4, Hossein Gholizadeh5, Seyed Mohammad Motahar6 & Ali Ordi7 1 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia 2 Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3 Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia 4 Department of Financial Sciences , University of Economic Sciences, Tehran, 1593656311, Iran 5 Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia 6 Faculty of Information Science and Technology,Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia 7 Universiti Teknologi Malaysia, Advance Informatics School (AIS), Kuala Lumpur, Malaysia Correspondence: Nader Ale Ebrahim, Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, 50603, Kuala Lumpur, Malaysia. Tel: 603-7967-7355. E-mail: [email protected]
Received: September 12, 2013 Accepted: October 12, 2013 Online Published: October 23, 2013 doi:10.5539/ies.v6n11p93 URL: http://dx.doi.org/10.5539/ies.v6n11p93
Abstract Due to the effect of citation impact on The Higher Education (THE) world university ranking system, most of the researchers are looking for some helpful techniques to increase their citation record. This paper by reviewing the relevant articles extracts 33 different ways for increasing the citations possibilities. The results show that the article visibility has tended to receive more download and citations. This is probably the first study to collect over 30 different ways to improve the citation record. Further study is needed to explore and expand these techniques in specific fields of study in order to make the results more precisely. Keywords: university ranking, improve citation, citation frequency, research impact, open access, h-index 1. Introduction The research output is an essential part of an institution’s measure and evaluation of research quality. Previously, the number of publication and journal impact factors were the means to derive research ratings. Recent approach for rating research quality rely on the number of citations per article. Citation shows that how many times an article has been cited by other articles (Fooladi et al. 2013). Citations to research publications are a quality indicator, important for both the author and the affiliated university (Jones and Evans 2013). Most researchers are evaluated based upon their publications as well as the numbers of citations their publications receive. One of the key ways to increase citations is to expose the research output to the widest possible audience. If people are unaware of the research, they won’t be citing it. The more researchers and students in other fields who have heard about the research, the researcher will receive more citations. Citations to an article might strongly depend on the visibility, rather than the merit of the article (Marashi et al. 2013). Ale Ebrahim (2012) argues that publishing a high quality paper in scientific journals will be a halfway of receiving citation in the future. The rest of the way is advertising and disseminating the publications by using the proper “Research Tools”. Post-print publishing means to make peer-reviewed, published research articles freely available to anyone with an internet connection, often greatly increases the citation frequency of articles (LiU E-Press 2007). The following section introduces 33 ways which may help the researchers to improve the number of their citations.
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2. 33 Ways for Improving Citations 1. Use A Unique Name Consistently Throughout Academic Careers Authors are highly advised to use the same variation of their name consistently throughout their academic careers. If the name is a common name, consider adding your full middle name to distinguish it from other authors. Consistency enhances retrieval (Sarli and Holmes 2011). 2. Use a standardized institutional affiliation and address, using no abbreviations (Sarli and Holmes 2011). Standardization of author affiliation is important to make sure work can be attributed to the correct author and institution (Jones and Evans 2013). Providing accurate contact details are essential so that researchers can contact directly for queries, further information and discussions about the publication (Wong 2008). 3. Repeat key phrases in the abstract while writing naturally. Make some key phrases of your study and repeat them in the abstract page of your paper. Since search engines and citation trackers search the abstract of your article, the normal repetition of key words increases the chance of your paper to be retrieved more easily (Sarli and Holmes, 2011; Jones and Evans, 2013). 4. Assign keyword terms to the manuscript (Sarli and Holmes 2011). Using keywords is a vital part of abstract writing, because of the practice of retrieving information electronically: keywords act as the search term. Use keywords that are specific, and that reflect what is essential about the paper. Put yourself in the position of someone researching in your field: what would you look for? Consider also whether you can use any of the current "buzzwords" (Emerald Guide 2012). 5. Make a unique phrase that reflects author's research interest and use it throughout academic life. Add the name of study in the title of all publications and use the same title/ name consistently (Sarli and Holmes 2011). 6. Publish in journal with high impact factor (Vanclay 2013). The most effective strategy to increase citation rates is publishing in a journal with higher impact factor (Vanclay 2013). Dhawan and Gupta (2005) studied 1101 papers and found that articles published in high impact factor journals increase the probability of getting cited. 7. Self-archive articles. Free online availability increases a paper's impact (Lawrence 2001); therefore, maximize the visibility of your research by making copies of your articles available online (Jones and Evans 2013). Gargouri et al. (2010) have made a strong and a declarative link between self-archiving and increased citation performance. 8. Keep your professional web pages and published lists up to date (Jones and Evans 2013). The advantage of self-archive on the web and make a link between published lists is obvious. Freely accessible articles increase citations by 50% or more (Harnad 2006). 9. Make your research easy to find, especially for online searchers (Jones and Evans 2013). Jamali and Nikzad (2011) investigated 2172 articles and found that there is a positive relationship between the number of downloads and citations. Research shows that there is a correlation between highly cited articles and the likelihood of it being online (Vaughan and Shaw 2003). 10. Open Access (OA) increases citation rate (MacCallum and Parthasarathy 2006). Free access increases citation rates, searching online is more efficient and following hyperlinks quickly leads researchers to their prevailing opinion (Evans 2008). Open Access has a positive impact on growth of citations (Table 1) (Swan 2010). 11. Deposit paper in Open Access repository (Frost 2009). For example, Ray Frost is a chemist who publishes prolifically. About three years ago, he began depositing his articles in the Queensland University of Technology (QUT) repository. So far, he has deposited over 300 of his articles. Figure 1 (derived data from the Web of Science) shows the patterns of publication and citations to those publications. When Ray started putting his articles into the QUT repository, the numbers of citations began to take off. The latest count is 1200 in one year. Even though Ray’s publication rate went up a bit over this period, the increase in citations is impressive (Frost 2009).
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Table 1. Effect of Open Access (OA) to increase the level of citations (Swan 2010) Size of OA citation advantage when found % increase in citations with Open Access (and where explicitly stated by discipline)
Physics/astronomy 170 to 580 Mathematics 35 to 91 Biology -5 to 36 Electrical engineering 51 Computer science 157 Political science 86 Philosophy 45 Medicine 300 to 450 Communication studies (IT) 200 Agricultural sciences 200 to 600
Figure 1. The patterns of citations to Ray Frost’s articles that are deposited in the Queensland University of Technology (QUT) repository (Frost 2009)
12. Publish with international authors (Pislyakov and Shukshina 2012) Citation analysis shows that papers with international co-authors are cited up to four times more often than those without international co-authors (Jones and Evans 2013). Krause (2009) argued that articles published with multi-countries or multi-institutes collaborations get cited more. Authors who are often involved in international collaboration received more citations (Aksnes 2003). 13. Team-authored articles get cited more (Krause 2009) Wuchty et al. (2007) have used 19.9 million papers over 5o years and demonstrated that team-authored articles typically produce more frequently cited research than individuals. A recent study by Cotropia and Petherbridge (2013) in law review articles which were published within two decades also demonstrated that team research is on average more frequently cited than individual research. Typically high cited articles are authored by a large number of scientists (Aksnes 2003). 14. Use more references There is a ridiculously strong relationship between the number of citations a paper receives and the number of its references (Corbyn 2010). 15. Publish a longer paper A longer paper gathers more citations (Ball 2008; Abt 1998). Hamrick et al. (2010) indicated that longer papers are associated with more citations. 16. Publish papers with a Nobel laureates (Ball 2011)
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Some landmark papers of Nobel laureates quite quickly give their authors a sudden boost in citation rate and this boost extends to the author's earlier papers too, even if they were in unrelated areas (Ball 2011). 17. Contribute to Wikipedia (SAGE 2012) Try to contribute in Wikipedia. As a good example, one paper (Nader Ale Ebrahim et al. 2009) that was used as a reference in defining virtual teams in Wikipedia has received significant citations in comparison to the rest of the articles from the same author. 18. Start blogging (SAGE 2012) Use blogs and podcasts to leverage on-going researcher discussion on the Internet (Taylor & Francis Group 2012a). Web 2.0 tools such as wikis and blogs can be created to inform, describe and link people’s research interests and publications (Wong 2008). Authors are encouraged to promote their papers through the addition of links which web search engines such as Google take particular notice for their page ranks (Smith 2005). 19. Join academic social networking sites (Taylor & Francis Group 2012b) Increasing the availability of articles through social networking sites broadens dissemination, increases use, and enhances professional visibility which lead to increased citations and usage. Academica is an online social reference tool that allows reference sharing among academics and researchers. Alternatively, researchers may use Citeulike to share their interests in research publications (Wong 2008). Academica, Citeulike, ResearchGate and Linkedin are just a few examples of knowledge sharing tools to make others aware of research articles that may be of relevance to authors and hence get cited. 20. Write a review paper Reviews are more likely to be cited than original research papers. Some types of articles including editorials, letters to editors, news items, meeting abstracts, and case studies are generally poorly cited (Taylor & Francis Group 2012a). Authors seeking to be well cited should aim to write comprehensive and substantial review articles, and submit them to journals with a high impact factor that carry previous articles on the topic (Vanclay 2013). 21. Papers published after having first been rejected elsewhere receive significantly more citations (Ball 2012) Submission history affected post-publication impact, resubmissions from other journals received significantly more citations than first-intent submissions (Calcagno et al. 2012). 22. Papers with a larger number of “callouts” be likely to receive a higher number of citations (Hamrick et al. 2010) A “callout” is a phrase or sentence from the paper that is displayed in a different font, somewhere in the paper. Generally, callouts are inserted by the editorial staff to call attention to potentially interesting aspects of a paper (Hamrick et al. 2010). 23. Avoid to select a question type of title Jamali and Nikzad (2011) investigated 2172 articles and found different types of title effects on the number of downloads and citations. Especially articles with question type titles tend to be downloaded more but cited less than the others. 24. Sharing detailed research data Open data (Publicly-available datasets) are significantly associated with a 69% increase in citations to articles that accompany the data. This correlation is independent of Journal Impact Factor, country of authors and time since publication (Piwowar et al. 2007; Swan 2010). 25. Publish across disciplines Publishing across disciplines has been found to increase citation e.g. chemistry, biological science and physics (Ortega and Antell 2006). 26. Present a working paper (Taylor & Francis Group 2012a) Try to go to a prestigious conference and present some parts of your research or publish working paper. Working papers are freely available before and after the articles are published. Researchers may upload their working papers into open access repositories including the personal websites or more formal repositories such as arXiv and SSRN (McCabe 2011). 27. Publish your article in one of the journals everyone in your discipline reads (Taylor & Francis Group 2012a)
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Choosing a journal that matches with a researcher’s field of study is thus very important because it makes it more likely that the article receives more citation. A journal which covers a broad range of disciplines may be the best. 28. Publicize yourself - link your latest published article to your email signature (Taylor & Francis Group 2012a) A great way to spread researchers’ outputs and get extra attention of email recipient is to add a link to the latest publication. This little section of contact information that most people ignore, provides a good platform for publication marketing. 29. Publish your work in a journal with the highest number of abstracting and indexing (Nader Ale Ebrahim 2012) Abstracting and indexing services generate visibility for the material published in most journals (Garfield and Merton 1979). Citation potential increases by attributing to the high visibility of scientific materials. Therefore, a journal with the highest number of abstracting and indexing in different databases can be a good target. 30. Create a podcast describing the research project Research is not just text and figures. Create a podcast describing the research project and submit the podcast to YouTube or Vimeo (Sarli and Holmes 2011). Podcasts can describe the research efforts. Video is an increasingly important way for researchers to communicate their results and welcome submissions of podcasts from authors and editors (Sarli and Holmes 2011). 31. Make an online CV Like ORCID or ResearcherID Online CV makes a link between the list of published papers and open access versions of relevant articles (Sahu 2005). Online CV increases researchers’ output visibility to the academic community. 32. Publish tutorials papers Tutorial paper is “a paper that organizes and introduces work in the field. A tutorial paper assumes its audience is inexpert; it emphasizes the basic concepts of the field and provides concrete examples that embody these concepts (ACM 2013)”. Tutorials papers tend to have a higher number of citations (Hamrick et al. 2010). and finally; 33. Use all “Enhancing Visibility and Impact” tools which are available on http://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim Familiarity with academic advertisement tools allows the researcher to increase his/her h-index in the short time. H-index shows the academicians’ influences in the specified field of research (Aghaei Chadegani et al. 2013). Therefore, a person with higher levels of h-index has higher quality publications with high amount of citations (Nader Ale Ebrahim 2012). The advertisement section of the above mind map includes the tools which can assist the researchers to disseminate and increase visibility of their published papers. 3. Conclusion Publishing research output in high-impact journals is a primary concern of the researchers. The researchers also need to consider different ways to receive more citations after publishing a paper. When their works are published, they are concerned about citation which is directly related to the paper’s quality and visibility. The researchers cannot increase the quality of their published papers; therefore, they can apply some of these 33 key points to increase the visibility of their published papers. References Abt, H. A. (1998). Why some papers have long citation lifetimes. [10.1038/27355]. Nature, 395(6704), 756-757. http://dx.doi.org/10.1038/27355 ACM. (2013). ACM Computing Surveys. Retrieved May 30, 2013, from http://csur.acm.org/author_info.html Aghaei Chadegani, A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., & Farhadi, M., et al. (2013). A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases. Asian Social Science, 9(5), 18-26. http://dx.doi.org/10.5539/ass.v9n5p18 Aksnes, D. W. (2003). Characteristics of highly cited papers. Research Evaluation 12(3), 159-170. http://dx.doi.org/10.3152/147154403781776645 Ale Ebrahim, N. (2012). Publication Marketing Tools “Enhancing Research Visibility and Improving Citations”. Research Tools in Education Series, 1(2), 1-86.
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Copyrights Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
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Modern Applied Science; Vol. 8, No. 5; 2014 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Equality of Google Scholar with Web of Science Citations: Case of Malaysian Engineering Highly Cited Papers
Nader Ale Ebrahim1, Hadi Salehi2, Mohamed Amin Embi3, Mahmoud Danaee4, Marjan Mohammadjafari5, Azam Zavvari6, Masoud Shakiba6 & Masoomeh Shahbazi-Moghadam7 1 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia 2 Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3 Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Malaysia 4 Faculty of Agriculture, Roudehen Branch, Islamic Azad University, Roudehen, Iran 5 Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran 6 Center for Software Technology and Management, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia 7 Perdana School of Science, Technology and Innovation Policy, Universiti Teknologi Malaysia Correspondence: Hadi Salehi, Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran. E-mail: [email protected]
Received: June 1, 2014 Accepted: June 23, 2014 Online Published: August 6, 2014 doi:10.5539/mas.v8n5p63 URL: http://dx.doi.org/10.5539/mas.v8n5p63
Abstract This study uses citation analysis from two citation tracking databases, Google Scholar (GS) and ISI Web of Science, in order to test the correlation between them and examine the effect of the number of paper versions on citations. The data were retrieved from the Essential Science Indicators and Google Scholar for 101 highly cited papers from Malaysia in the field of engineering. An equation for estimating the citation in ISI based on Google scholar is offered. The results show a significant and positive relationship between both citation in Google Scholar and ISI Web of Science with the number of versions. This relationship is higher between versions and ISI citations (r = 0.395, p<0.01) than between versions and Google Scholar citations (r = 0.315, p<0.01). Free access to data provided by Google Scholar and the correlation to get ISI citation which is costly, allow more transparency in tenure reviews, funding agency and other science policy, to count citations and analyze scholars’ performance more precisely. Keywords: bibliometrics, citation analysis, evaluations, equivalence, Google Scholar, High cited, ISI Web of Science, research tools, H-index 1. Introduction Citation index as a type of Bibliometrics method traces the references in a published article. It shows that how many times an article has been cited by other articles (Fooladi et al., 2013). Citations are applied to evaluate the academic performance and the importance of information contained in an article (Zhang, 2009). This feature helps researchers get a preliminary idea of the articles and research that make an impact in a field of interest. The avenues to evaluate citation tracking have greatly increased in the past years (Kear & Colbert-Lewis, 2011). Citation analysis was monopolized for decades by the system developed by Eugene Garfield at the Institute for Scientific Information (ISI) now owned by Thomson Reuter Scientific (Bensman, 2011). ISI Web of Science is a publication and citation database which covers all domains of science and social science for many years (Aghaei Chadegani et al., 2013). In 2004, two competitors emerged, Scopus and Google Scholar (Bakkalbasi, Bauer, Glover, & Wang, 2006). Google Inc. released the beta version of ‘Google Scholar’ (GS) (http://scholar.google.com) in November 2004 (Pauly & Stergiou, 2005). These three tools, ISI from Thomson Reuters, Google Scholar (GS) from Google Inc. and Scopus from Elsevier are used by academics to track their citation rates. Access to ISI Web of Science is subscription-based service while GS provides a free alternative to retrieve the citation counts. Therefore, the researchers need to estimate their citation in ISI by knowing the GS
63 www.ccsenet.org/mas Modern Applied Science Vol. 8, No. 5; 2014 citation counts. On the other hand, publishing a research paper in a scholarly journal is necessary but not sufficient for receiving citations in the future (Nader Ale Ebrahim, 2013). The paper should be visible to the relevant users and authors in order to get citations. The visibility of the paper is defined by the number of paper versions which are available in the Google Scholar database. The number of citations will be limited to the versions of the published article on the web. The literature has shown increased visibility by making research outputs available through open access repositories, wider access results and higher citation impact (Nader Ale Ebrahim et al., 2014; Amancio, Oliveira Jr, & da Fontoura Costa, 2012; Antelman, 2004; Ertürk & Şengül, 2012; Hardy, Oppenheim, Brody, & Hitchcock, 2005). A paper has greater chance of becoming highly cited whenever it has more visibility (Nader Ale Ebrahim et al., 2013; Egghe, Guns, & Rousseau, 2013). The objectives of this paper are two-fold. The first objective is to find the correlation between Google Scholar and ISI citation in the highly cited papers. The second objective is to find a relationship between the paper availability and the number of citations. 2. Google Scholar & Web of Science Citations The citation facility of Google Scholar is a potential new tool for Bibliometrics (Kousha & Thelwall, 2007). Google Scholar, is a free-of-charge by the giant Google search engine, has been suggested as an alternative or complementary resource to the commercial citation databases like Web of Knowledge (ISI/Thomson) or Scopus (Elsevier) (Aguillo, 2011). Google Scholar provides Bibliometrics information on a wide range of scholarly journals, and other published material, such as peer-reviewed papers, theses, books, abstracts and articles, from academic publishers, professional societies, preprint repositories, universities and other scholarly organizations (Orduña-Malea & Delgado López-Cózar, 2014). GS also introduced two new services in recent years: Google Scholar Author Citation Tracker in 2011 and Google Scholar Metrics for Publications in April 2012 (Jacso, 2012). Perhaps some of these documents would not otherwise be indexed by search engines such as Google, so they would be "invisible" to web searchers, and clearly some would be similarly invisible to Web of Science users, since it is dominated by academic journals (Kousha & Thelwall, 2007). On the other hand, the Thomson Reuters/Institute for Scientific Information databases (ISI) or Web of Science database (actually there is ambiguity between different names of former ISI), include three databases: Science Citation Index/Science Citation Index Expanded (SCI/SCIE) (SCIE is the online version of SCI), Social Science Citation Index (SSC) and Arts and Humanities Citation Index (AHCI) (Larsen & von Ins, 2010). Since 1964 the Science Citation Index (SCI) has been a leading tool in indexing (Garfield, 1972). Few studies have been done to find a correlation between GS with WoS citations. Cabezas-Clavijo and Delgado-Lopez-Cozar (2013) found that the average h-index values in Google Scholar are almost 30% higher than those obtained in ISI Web of Science, and about 15% higher than those collected by Scopus. GS citation data differed greatly from the findings using citations from the fee-based databases such as ISI Web of Science (Bornmann et al., 2009). Google Scholar overestimates the number of citable articles (in comparison with formal citation services such as Scopus and Thomson Reuters) because of the automated way it collects data, including ‘grey’ literature such as theses (Hooper, 2012). The first objective of this study is to find the correlation between Google Scholar and ISI citation in the highly cited papers. 3. Visibility and Citation Impact Nader Ale Ebrahim et al. (2014) based on a case study confirmed that the article visibility will greatly improve the citation impact. The journal visibility has an important influence on the journal citation impact (Yue & Wilson, 2004). Therefore, greater visibility caused higher citation impact (Zheng et al., 2012). In contrast, lack of visibility has condensed a significant citation impact (Rotich & Musakali, 2013). Nader Ale Ebrahim et al. (2013) by reviewing the relevant papers extracts 33 different ways for increasing the citations possibilities. The results show that the article visibility has tended to receive more download and citations. In order to improve the visibility of scholars’ works and make them relevant on the academic scene, electronic publishing will be advisable. This provides the potential to readers to search and locate the articles at minimum time within one journal or across multiple journals. This includes publishing articles in journals that are reputable and listed in various databases and peer reviewed (Rotich & Musakali, 2013). Free online availability substantially increases a paper’s impact (Lawrence, 2001a). Lawrence (2001a, 2001b) demonstrated a correlation between the likelihood of online availability of the full-text article and the total number of citations. He further showed that the relative citation counts for articles available online are on average 336% higher than those for articles not found online (Craig, Plume, McVeigh, Pringle, & Amin, 2007). However, there are limited resources to explain the relationship between the paper availability and the number of citations (Craig et al., 2007; Lawrence, 2001b; McCabe & Snyder, 2013; Solomon, Laakso, & Björk, 2013).
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None of them discussed about the relationship between the number of versions, and citation. The number of “versions” will be shown in any Google Scholar search result. Figure 1 shows 34 different versions of an article entitled “Virtual Teams: a Literature Review (Nader Ale Ebrahim, Ahmed, & Taha, 2009)” and number of citations. The second objective of this research is to find a relationship between the paper availability and the number of citations.
Figure 1. The number of “versions” in the Google Scholar search result
4. Methodology Highly cited papers from Malaysia in the field of engineering were retrieved from the Essential Science Indicators (ESI) which is one the Web of Knowledge (WoK) databases. ESI provides access to a comprehensive compilation of scientists’ performance statistics and science trend data derived from WoK Thomson Reuters databases. Total citation counts and cites per paper are indicators of influence and impact of each paper. There is a threshold to select highly cited papers according to the baseline data in ESI. This threshold is different from one discipline to another one. ESI rankings are determined for the most cited authors, institutions, countries, and journals (The Thomson Corporation, 2013). The paper must be published within the last 10-year plus four-month period (January 1, 2003-April 30, 2013) and must be cited above threshold level, in order to be selected. Essential Science Indicators data used in this research have been updated as of July 1, 2013. Google Scholar which is a free online database was used for deriving the number of citations and versions of the ESI highly cited papers. The data from ESI was collected on 29 July 2013 and Google Scholar data was collected on 31 July 2013. The total numbers of 101 papers were listed in ESI as highly cited papers from Malaysia in the field of engineering. The lists of 101 papers were retrieved from ESI database and then were exported to an Excel sheet. A search engine was developed to get the number of citations and versions from Google Scholar. This gadget assisted the present researchers to collect the data more preciously and faster than searching for the papers one by one. The Statistical Package for the Social Sciences (SPSS) was used for analyzing the data. The results are illustrated in the following section. 5. Results and Discussion The number of citations which were derived from Web of Knowledge platform hereafter are called ISI citation. To study the relationship among the number of citations in Google scholar and ISI and the number of versions, correlation coefficients were computed. Table 1 shows descriptive statistics of the variables.
Table 1. Descriptive statistic of variables N Minimum Maximum Mean Std. Deviation Versions 101 2 28 5.62 3.078 Cited in Google Scholar 101 4 348 80.76 71.718 ISI citation 101 5 189 43.15 36.076
As both numbers of citations in Google scholar and ISI were distributed normally, Pearson correlation coefficient (r) was used and the results showed a very high positive and significant association (r = 0.932 , P<0.01) between the number of citations in Google scholar and ISI for the articles that were published during 2006 to 2012 from Malaysia in the field of engineering. To study the relationship between both citation and the number of versions,
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Spearman Rho was used due to the non-normal distribution of the versions. The results showed a significant and positive relationship between both citations in Google Scholar and ISI with the number of versions. This relationship was higher between versions and ISI citations (r = 0.395, p<0.01) than between versions and Google Scholar citations (r = 0.315, p<0.01). Linear regression was also applied to predict the number of citations in ISI based on Google Scholar citations. The results showed a very high predictability (R2 = 0.836) for the linear model (see
Figure 2) which was significant (F = 511.63, p<0.01). Therefore, the final equation for estimating the citation in ISI based on Google Scholar is:
ISI Citation = 5.961 + 0.460 (Google Scholar citation)
Figure 2. Scatter diagram between ISI citation and Google Scholar citation
To study the effect of the number of versions on both citations in Google Scholar and ISI, simple linear regression was applied. The results indicated that the number of versions had a significant positive effect on citations in both databases (see Table 2 and Table 3).
Table 2. Summary of regression analysis results R Square F β t p Model a 0.276 39.12** 0.532 6.255 <0.01 Model b 0.272 38.316** 0.528 6.19 <0.01 Predictors: Versions a: Dependent Variable: Cited in Google Scholar, b: Dependent Variable: ISI citation
Table 3. Descriptive statistics of variables - Year Year N Versions Cited in Google Scholar ISI citation Mean SD Mean SD Mean SD Before 2009 20 7.75 5.25 152.85 103.741 79.8 46.6 2009 26 6.08 1.695 101.19 38.948 56.96 20.577 2010 18 5.11 2.193 70.17 50.097 41.44 26.86
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2011 16 4.31 1.352 49.25 33.66 21.31 12.015 2012 21 4.48 2.089 19.9 9.518 9.24 3.315 A comparison between Google Scholar and ISI citation for highly cited papers from Malaysia in the field of engineering (see Figure 3) shows that the citation counts in Google Scholar are always higher than the number of citations in ISI.
Figure 3. Comparison between Google Scholar and ISI citations
6. Conclusion The number of publications and the number of citations in ISI Web of Science are used to measure the researchers’ scientific performance and their research impact. However, these numbers are not freely available. Therefore, the offered equation can be used as a reference to convert the number of Google Scholar citations to ISI citations. On the other hand, the number of versions of both systems has a significant positive effect on the number of citations. This finding supports other researchers’ (Amancio et al., 2012; Antelman, 2004; Egghe et al., 2013; Ertürk & Şengül, 2012; Hardy et al., 2005) findings related to the paper visibility. The results of this study indicate that there is a strong correlation between the number of citations in Google Scholar and ISI Web of Science. Therefore, the researchers can increase the impact of their research by increasing the visibility of their research papers (or paper versions). Future study is needed to determine the relationship between citation counts on the other databases such as Microsoft Academic Research, Scopus, SiteSeer index and ISI by considering journal article and conference papers. Reference Aghaei Chadegani, A., Salehi, H., Yunus, M. M., Farhadi, H., Fooladi, M., Farhadi, M., & Ale Ebrahim, N. (2013). A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases. Asian Social Science, 9(5), 18-26. http://dx.doi.org/10.5539/ass.v9n5p18 Aguillo, I. F. (2011). Is Google Scholar useful for Bibliometrics? A Webometric Analysis. In E. Noyons, P. Ngulube, & J. Leta (Eds.), Proceedings of Issi 2011: The 13th Conference of the International Society for Scientometrics and Informetrics, Vols 1 and 2 (pp. 19-25). Leuven: Int Soc Scientometrics & Informetrics-Issi. Ale Ebrahim, N. (2013). Introduction to the Research Tools Mind Map. Research World, 10(4), 1-3. http://dx.doi.org/10.5281/zenodo.7712 Ale Ebrahim, N., Ahmed, S., & Taha, Z. (2009). Virtual Teams: a Literature Review. Australian Journal of Basic and Applied Sciences, 3(3), 2653-2669. http://dx.doi.org/10.6084/m9.figshare.1067906 Ale Ebrahim, N., Salehi, H., Embi, M. A., Habibi Tanha, F., Gholizadeh, H., & Motahar, S. M. (2014). Visibility
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Copyrights Copyright for this article is retained by the author(s), with first publication rights granted to the journal. This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
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International Education Studies; Vol. 7, No. 7; 2014 ISSN 1913-9020 E-ISSN 1913-9039 Published by Canadian Center of Science and Education Ethical and Unethical Methods of Plagiarism Prevention in Academic Writing
Kaveh Bakhtiyari1,2, Hadi Salehi3, Mohamed Amin Embi4, Masoud Shakiba2,5, Azam Zavvari2,5, Masoomeh Shahbazi-Moghadam6, Nader Ale Ebrahim7 & Marjan Mohammadjafari8 1 Department of Computer & Cognitive Science, Faculty of Engineering, University of Duisburg-Essen, Duisburg, Germany 2 Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi, Malaysia 3 Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 4 Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Bangi, Malaysia 5 Industrial Computing Group, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia 6 Perdana School of Science, Technology and Innovation Policy, Universiti Teknologi Malaysia, Malaysia 7 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia 8 Department of Industrial Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran Correspondence: Kaveh Bakhtiyari, Department of Electrical, Electronics and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia. E-mail: [email protected]
Received: February 10, 2014 Accepted: May 13, 2014 Online Published: June 19, 2014 doi:10.5539/ies.v7n7p52 URL: http://dx.doi.org/10.5539/ies.v7n7p52
Abstract This paper discusses plagiarism origins, and the ethical solutions to prevent it. It also reviews some unethical approaches, which may be used to decrease the plagiarism rate in academic writings. We propose eight ethical techniques to avoid unconscious and accidental plagiarism in manuscripts without using online systems such as Turnitin and/or iThenticate for cross checking and plagiarism detection. The efficiency of the proposed techniques is evaluated on five different texts using students individually. After application of the techniques on the texts, they were checked by Turnitin to produce the plagiarism and similarity report. At the end, the “effective factor” of each method has been compared with each other; and the best result went to a hybrid combination of all techniques to avoid plagiarism. The hybrid of ethical methods decreased the plagiarism rate reported by Turnitin from nearly 100% to the average of 8.4% on 5 manuscripts. Keywords: plagiarism, plagiarism prevention, academic cheating, ethical approaches, unethical approaches 1. Introduction Academic publishing is a key factor for any person in academia. All research students, lecturers, professors and researchers in general tend to publish their works and the results of their studies in academic conference proceedings, books, journals or magazines (Hockenos, 2013). Even though getting a paper published is very important, the originality and ethic of the context of the published paper is very important as well. The most common problem in academic manuscripts is “Plagiarism” (Carrol, 2002; Hawley, 1984; Patel, Bakhtiyari, & Taghavi, 2011). Plagiarism has been defined by different resources. But one of the best definitions of plagiarism is represented on Dictionary.com web site: “an act or instance of using or closely imitating the language and thoughts of another author without authorization and the representation of that author's work as one's own, as by not crediting the original
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author.” (Dictionary.com, 2013) In Etymonline, it is also defined as “kidnapper, seducer, plunderer” which is originality since 1597. In American Heritage, it is called as “Literary theft”, and in Encyclopedia Britannica is known as “fraudulence, forgery, piracy-practices” (Encyclopedia-Britannica, 2013). Therefore, plagiarism is the act of copying someone else text, idea or language and publish it as it is his/her own work. It is very clear from the above definitions that plagiarism is against the law, and the authors should avoid plagiarism in their own manuscripts. Nowadays, plagiarism is growing along with increase of publications and academic papers. One of the most important criteria to evaluate the academics is their publications. Therefore, it is very important to help them to increase their publication quality, and make them aware of plagiarism consequences. This paper gives a specific attention to the authors, who plagiarize unconsciously without being aware of the plagiarism. Therefore, the present study is to propose some ethical techniques which can prevent or at least decrease the plagiarism. Beside the ethical techniques, we also present the unethical methods of masking plagiarism, which are employed by the authors who consciously plagiarize. However, in order to decrease the rate of plagiarism, we do not recommend using unethical methods which are not accepted in academia. But it is worth mentioning them to drag librarian and academic lecturers’ attention. To evaluate the efficiency of proposed ethical techniques, they are applied in five different texts using 16 students. The results had shown promising efficiency in plagiarism. 2. Plagiarism: Literature & Causes At the beginning, we discuss the causes of plagiarism, and analyze how an author might commit plagiarism. There are two types of authors who have plagiarism in their manuscripts. The first type is the direct plagiarizing that the author would plagiarize by duplicating another author’s text exactly as it is, and pretending as his/her own work. It is commonly known as copy-pasting in word processing applications. The second type is about authors who plagiarize unconsciously. There are many factors which may cause these problems. Some of these factors are as follows: 1) Uncited Ideas & Concepts: Scholars are used to read many articles to improve other methodologies in order to deliver a contribution in science. Using other researcher’s idea is considered as plagiarism unless his/her research article would be cited properly in the manuscript. Even though authors cite the other scholars’ research, they should avoid using the same words and language structures of the original text. 2) Many Authors: In many manuscripts, there might be many authors involved. However, not every single author is aware whether the others have been honest and ethical in their writings or not. In a collaborative work, each author is responsible for what he/she writes, but when it gets published all the authors are partially responsible for the whole published material. Usually all authors trust each other in publications. However, always the authors may not be honest. There is a famous quotation which says: “Sometimes too much trust kills you”. 3) Accidental Similarity: The next factor is when the text of the author has been closely similar to another author’s by chance. When an idea or a sentence is as usual as everybody uses in their own text, it might be possible that a sentence or a concept would be recognized as plagiarism in plagiarism detection systems. 4) Fixed Definitions: At almost all sciences, there are fixed definitions which authors write and cite them, and modification would make them a change or twist the meaning. 5) Cross-Text Plagiarism (Text Recycling): A piece of writing consists of several parts such as Introduction, Body and Conclusion. These parts are dependent on the manuscript goals, applications and styles. However, they might have more sections including Abstract, Evaluation, etc. Sometimes, authors need to repeat some concepts in various parts of the text. For example, in the abstract they have to talk about what they are presenting and discussing in the whole paper. Meanwhile, at the same time, each part of the abstract has been discussed in details in the body of the manuscript. Another example, in methodologies, evaluations, or even conclusion; the authors would definitely address the problems and issues which have been discussed and elaborated in the other sections (e.g. Introduction) (IEEE, 2011). These texts with the same meaning occurring in various places of the text might cause language and word similarity inside the manuscript. To elaborate, a sentence in introduction with the definition of the problem statement might be duplicated somewhere else in the paper (e.g. in Conclusion) with the exact language and sentence structure (iThenticate.com, 2011). This problem is a very common issue with plagiarism. The ability of writing a sentence with a same meaning in various styles and structures is an art, and it needs practice and authority in both language and science. Co-authoring and having the manuscript proofread help significantly to overcome this problem.
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6) Self-Plagiarism (Similarity): Researchers might publish more than one article from their own research. Even though they are different, they have many similarities in different sections of the articles. These papers may fall into plagiarism by the following three cases: a. Redundancy and Duplication: This is also known as dual publication. It refers to the publication of a single research article in more than one journal. Schein has done a research that 93 out of 660 studies (more than 14%) from 3 surgical major journals were in case of dual publication (Schein & Paladugu, 2001). Dual publication is not always considered as plagiarism. In two cases, dual publication is accepted and ethical. i) Summaries and abstracts which have been presented and published in a conference proceedings can get published in a journal with some extension on the original paper. This extension usually should be minimum 30%. ii) A published article can be republished in another language. But in the second publication, the existence of the original (first article) should be mentioned and addressed. b. Salami Slicing: Sometimes when the research has different aspects, the authors generate various articles on different sections of the research such as literature review and methodology. They may also split up the results (data fragmentation) in order to generate different research papers. In all these papers, the basics such as the Introduction and Problem Statements are very similar to each other. Therefore, writing the same concepts in different words is usually difficult. Some authors only copy the text from their own previous published papers. Besides data fragmentation, there is another issue called data augmentation. Data augmentation refers to the publication of the studies which have been already published, and subsequently, the authors would collect new data to improve the result and the contribution of the research. Having both results mixed together and get it published might easily mislead the readers (malpractice). c. Copyright Infringement: The act of copying a text from the author’s previous published manuscripts is another case of plagiarism. Although the authors have written the original text, it was published under the copyright of a publisher. According to the copyright rules and regulations, making a copy of the published text in whole section or a part is considered as plagiarism (or self-plagiarism). 7) Metadata: At the time of producing a document, the word processors (such as Microsoft Office Word) make some metadata inside the document, which are hidden from the eyes and print. The metadata consist of many useful information, and sometimes they cause headaches to the author (Hockenos, 2013; Mehta, Meyer, & Murgai, 2013). For example, in a Microsoft Word document file, the generated metadata contains the amount of times in minutes which took to complete the file, the last 10 authors of the file (last 10 computer names which edited the file), the company which has registered Microsoft Office product, and in some cases with the track changes turned on, the deleted texts could also be found and extracted. Dennis Rader was prosecuted because of murder, torture and bind. He was found and caught by police because his word processor left his company information in his sent document’s metadata (Anderson, 2013). The plagiarism as the problem, and its causes have been discussed. However the main challenge is to provide a solution on “How can we avoid the plagiarism?” 3. Plagiarism Prevention Methods and Techniques In the previous section, the driving factors of plagiarism in a manuscript were discussed. Here, few solutions are presented to avoid unwanted plagiarism. As it has been discussed earlier, there are two groups of authors with plagiarism. The first group of authors who are aware of plagiarism and what they do. For this group the only solution is providing them sufficient education on consequences of ethical and unethical writings. The second group includes the authors who plagiarize unconsciously without any intention to do so. Accordingly, there are two major methods to cover up plagiarism by these two groups of authors: 1) Unethical methods, 2) Ethical methods. Unethical methods provide the solutions to decreasing the plagiarism by bypassing the plagiarism detection software. In fact, the plagiarism has not been solved by these methods, but the software would report very low or even 0% of plagiarism and similarity. Ethical methods talk about how to change the text properly in order to decrease and to avoid the real amount of the plagiarism. The methods discussed in this paper are considered as the authors have followed the ethical behavior in their research and have cited all references correctly and
54 www.ccsenet.org/ies International Education Studies Vol. 7, No. 7; 2014 properly in their manuscripts. In the follow, the solutions, methods and techniques are discussed in both of unethical and ethical approaches. 3.1 Unethical Methods Unethical methods refer to the techniques in order to decrease the plagiarism rate without any effort to change the original text. These techniques aim to bypass plagiarism detection algorithms and to get a reasonable result of plagiarism on a highly plagiarized context (Gillam, Marinuzzi, & Ioannou, 2010). However, they are not decent and not recommended by any librarian. In this section, these methods are discussed and elaborated to inform authors, lecturers, librarians and students to avoid and to be mindful of these methods. Also to make sure that these techniques have not been applied to the produced manuscripts to be published. Besides the unethical methods, there is a new concept in publishing and writing industry called “Ghost writing”. Ghost writing means the action of getting other people to write on behalf of someone else. Although it is legal and common among politicians and famous people, it is prohibited in the academic world. There are ghost writers who write articles, books, academic assignments, or even university theses on behalf of a researcher for money. Even though using ghost writing service seems legal, easy and common in many areas, but it is considered as an unethical behavior in academia. 3.1.1 Mojibake Mojibake is a Japanese word means transformation, and here it literally means character transformation. Using Mojibake is only available in multi-layer documents such as PDF. A PDF document has a minimum of two layers. The first layer (top-layer) is visual, and it is visible to the reader and printer. This layer is similar to an image, and its text cannot be rendered. The second layer (bottom-layer) is a textual layer, and it contains the text of the visual layer. When a text is selected, copied or searched; the textual layer is used and it is shown on the visual layer (ISO, 2008). In PDF documents, it is possible to keep the visual layer, which is image based, and change the textual layer to the non-meaningful expressions. Figure 1 shows a sample of a PDF document which its textual layer is changed to Mojibake (Gillam et al., 2010).
Figure 1. A sample of a text and its Mojibake in a PDF document
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By having such PDF document submitted to plagiarism detection software, the system reads the textual layer and checks its plagiarism rate which would be equal to 0%. This method is useful for the copyright protection in order to prevent PDF documents’ texts from being copied and reused in other documents. 3.1.2 Cyrillic Letters Coincidentally, some of the letters in Latin and Cyrillic look the same, but they are actually different. There are three letters of “a”, “o” and “e” which they exist in both Latin and Cyrillic and they all look pretty much the same in both alphabetical systems. These letters have also different values in ASCII and UNICODE for computer systems. It means that they would be recognized as different characters in computer applications. As an unethical action, the letters of “a”, “o” and “e” can be replaced with their similar Cyrillic character in a whole text and those letters with the replaced characters will be invisible and unsearchable for plagiarism detection software (Gillam et al., 2010). 3.1.3 Space Replacement In a text, every word is separated from the adjacent words with a simple space. In fact, spaces define the words. If there is no space in a text, it means that there is only one word even with hundreds of characters. This method tries to eliminate the spaces of a text for plagiarism detection software, but on the other hand, make it readable to the humans. If the spaces would be replaced with dots or some Chinese characters and then recolored to white, they will not be visible in rich-text area. But in the plagiarism detection software, it would be considered as only one word as it only processes the plain text and eliminates the rich-text’s styles. In the following, there is the main text and its space-replaced version (Patel et al., 2011).
Original Text: “Before Unicode, it was necessary to match text encoding with a font using the same encoding system. Failure to do this produced unreadable gibberish whose specific appearance varied depending on the exact combination of text encoding and font encoding.”
Text with spaces replaced with a Chinese character: Before 画 Unicode,画 it 画 was 画 necessary 画 to 画 match 画 text 画 encoding 画 with 画 a 画 font 画 using 画 the 画 same 画 encoding 画 system.画 Failure 画 to 画 do 画 this 画 produced 画 unreadable 画 gibberish 画 whose 画 specific 画 appearance 画 varied 画 depending 画 on 画 the 画 exact 画 combination 画 of 画 text 画 encoding 画 and 画 font 画 encoding.
The second text with 39 words is considered as a single word for anti-plagiarism software with the same number of characters as there is no space in the whole text. 3.1.4 Automatic Translation System Translation (SysTran) is a translation of a text from one language into another by using computer systems (Google, 2011; SysTranSoft.com, 2011). These systems have been improving every day and more users are using such systems. Even though these systems cannot translate the text accurately as a human does, they are very fast and their results are reliable. Using SysTran for an academic manuscript is a double-sided blade, which has ethical and unethical uses. There are lots of research studies available in other languages, and they have not been translated into English. Using SysTran to translate the research and publish as their own work is considered as an unethical approach. Because what it has been claimed is not the author’s original work. 3.1.5 Synonyms Replacement Most of word processors are equipped with a dictionary or a thesaurus to suggest synonyms and antonyms for the words. This feature is very useful for the author to replace the similar words in a paragraph with their synonyms. Although this feature helps, it can be used to reduce plagiarism from a copied text. If some words in a copied text would be replaced with their synonyms, the plagiarism detection systems usually fail to detect them. However, the final text has been changed, the original structure of the context is still the same, and it is considered as an unethical method to reduce the plagiarism.
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3.1.6 Text Image All of the available plagiarism detection systems only process the text of a manuscript, and they simply ignore the images. If the authors put the text in an image, they would be skipped because the system cannot recognize the text in the images of a manuscript (Patel et al., 2011). This detection is not only difficult for the system, but also having a text image can be quite tough for humans to be recognized manually. For example, a text image in a Word document is pretty clear to be identified, but in a PDF document, it is not easy to be found. 3.2 Ethical Methods Ethical methods focus on the text words and structures to change them properly and to decrease the similarity percentage as much as possible. These techniques should be used and applied with consideration of citing the references correctly and properly in the manuscripts. These methods do not rely on the weaknesses of the plagiarism detection algorithms. 3.2.1 Reading from Many Sources When a researcher intends to read about a topic, usually there are loads of references to be read. Researchers might get the point by reading only one reference, and reading the other similar articles might look time wasting in some points. There is a general psychological aspect of reading only one single reference. When a person reads a text, he/she would respond by using the same structure when he/she is questioned. For example, imagine a person who has watched a movie or read a book. Now he is retelling it to the others. He might use the same or very close dialogues of the actors unconsciously to describe what has happened in the movie. Although they do not want to tell the exact words and they have not memorized them; they repeat what they have heard (or read). But when a person reads the same concept in many resources, his brain would make an imaginative conceptual structure in his mind. While he decides to write, his brain discusses what exists as a structural model of the concept in his brain and not the words and language structures. Reading from many resources will cause the reader to have the concept of context fixed in his mind. However, reading a single text for many times would have the words of context fixed in mind, and not the concept. People usually do this (reading a single text for many times) to memorize a text. 3.2.2 Writing after a Few Days of Reading It is quite common to write exactly what you have just read. Writing instantly after reading has the benefit of being accurate and not missing any important part, but it also increases the chance of facing with plagiarism in the text. According to the forgetting curve of human memory by Hermann Ebbinghaus, what it has been read will be forgotten a day after by 20% (Ebbinghaus, 1913).
Figure 2. Forgetting curve of human brain by Hermann Ebbinghaus
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Figure 2 shows the forgetting curve that after a day, 20% of what we have learnt is lost. If we do not review it in 3 days, we have almost forgotten 40%, and in almost a week, it is completely gone. But if after a day, we review it for a few minutes, we can get it back to 100% again. This time the slope of the forgetting curve is less than the previous time. Therefore, after 2 days of the review, we might lose 20% which was possible previously in only 1 day. This chart shows that after 4 times of periodic revision, it would be fixed in our minds. By using this curve, it can be concluded that it is preferable to write a few days after you have read a reference. In this way, the exact words and language structure will not be in your mind anymore, and your brain will make up the new sentences automatically. 3.2.3 Using Thesaurus/Dictionary A plagiarized sentence is an expression with the duplicated words. So, replacing the words with synonyms and antonyms can help the authors to decrease the chance and amount of similarity in the context. Using dictionaries and thesauruses are good tools for this purpose. Beside the paper-based dictionaries, almost most of the word processing software is featured with an integrated dictionary to provide word replacements. Using these tools not only decrease the plagiarism rate, but also it beautifies and makes fluent texts by replacing the repeated words in a paragraph. This technique is not ethical by itself, because replacing the words do not change the sentence structures. But having the other approaches to avoid plagiarism next to this method is ethical. 3.2.4 As Many Proofread as Possible Many authors would just have their own work submitted for publication just after being finished with writing. Of course, they might be very good in writing or confident in the science, but having the paper proofread as many as possible would also make some minor changes and improvements in the language structure of the paper. These changes would definitely eliminate the plagiarism and similarity rate. There are three issues that the authors should be aware of them. Firstly, they primarily should get people with the same research discipline to proofread the text. Therefore, they only would modify the context as the meaning remains the same. Asking various people with various research areas can just make amphibologies and opacities in the context. The second issue is that spreading the paper to many people can increase the risk of research piracy. Some of the colleagues may publish the manuscript before the main author publishes the paper. Therefore, the authors should find the people who are trustworthy and reliable. The third issue is to remember to review the paper every time after the proof readings. Even if the authors trust the knowledge of colleagues who have proofread the paper, still they should check to see everything has been done correctly. It is very possible to make mistakes or twist the meanings when they edit a manuscript. 3.2.5 Using Machine Translators Some people find it very tricky to write a sentence with a same meaning in several structures (paraphrase); sometimes they cannot find any other solution rather than copying from the original location. Finding people to paraphrase them is not always possible, or it is time consuming. Using machine translators is a very easy but not a fast solution. Computer translators use Natural Language Processing (NLP) techniques. NLP is a research area and it is not going to be discussed in this paper. But here we get to know how it works in multi lingual translators. They choose a language (usually English) as a main language, and they translate every other language from and into this language. For example, if a user would like to translate a text from French into German, the system firstly translates French into English, and then English into German. The reason of this action is based on the graph mathematics. Making the translation rules of each language into another one in a multi-lingual translator will make a mesh network of relations of languages and rules, which makes a really huge set of rules. A multi-lingual translator with the support of 10 languages would only need 9 sets of rules, which are between 9 languages and the one as the main language. But making a mesh network of sets for 10 languages is equal to 45 sets of rules as following Equation 1.