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Asian Social ; Vol. 9, No. 5; 2013 ISSN 1911-2017 E-ISSN 1911-2025 Published by Canadian Center of Science and A Comparison between Two Main Academic Collections: and Databases

Arezoo Aghaei Chadegani1, Hadi Salehi2, Melor Md Yunus3, Hadi Farhadi4, Masood Fooladi1, Maryam Farhadi1 & Nader Ale Ebrahim5 1 Department of , Mobarakeh Branch, Islamic Azad , Mobarakeh, Isfahan, Iran 2 Faculty of Literature and , Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 3 Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Malaysia 4 School of and Human , Faculty of Social and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia 5 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 has been 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 received to indicate the journals impact, prestige or influence. This 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, , citations, provenance, coverage, searching, tracking, , indexing, h-index, researcher profile, researcher ID 1. 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, 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 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 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 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 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 , 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 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 created by in 1960s. WOS includes above 10,000 journals and comprises of seven different citation databases including different information collected from journals, conferences, reports, and series. WOS citation databases are Social Sciences Citation Index (SSCI), Expanded (SCI Expanded), Conference Proceedings Citation Index Science (CPCI-S), and Humanities Citation Index (A&HCI) and Conference Proceedings Citation Index-Social Sciences and Humanities (CPCI-SSH). It has also two 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 (Kuchi, 2004). Scopus covers more than 49 million records including trade publications, open-access journals, and . Almost 80% of these records include abstract. It contains 20,500 peer-reviewed journals from 5,000 publishers, together with 1200 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 . Based on Scopus report more than 50% of its information are from the Middle East, Europe and . 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 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 types that permit the user to easily narrow their searching. Both databases sort the results by parameters such as; first , 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 . The resulting 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 ’ 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 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 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 , 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, ’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 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 journals. However, Escalona et al. (2010) find that there is a high similarity between WOS and Scopus in other fields such as Chemical . 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 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 . 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 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 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 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 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 . 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 , 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 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 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 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 , 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,

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 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. . 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 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 of scientists and researchers. Therefore, editors have a high tendency to increase the IF of their journals. In 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- 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 , 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 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 , 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 in which some types of researches are undervalued and falsifies the total contribution of each (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 . 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 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 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 . 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, , 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 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 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- 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 : 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). : 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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selection of references in scientific articles. International Journal of Impotence Research, 19, 443-445. http://dx.doi.org/10.1038/sj.ijir.3901583 Falagas, M. E., & Kavvadia, P. (2006). Eigenlob: Self-citation in biomedical journals. Federation of American Societies for Experimental Journal, 20, 1039-1042. http://dx.doi.org/10.1096/fj.06-0603ufm Falagas, M. E., Kouranos, V. D., Arencibia-Jorge, R., & Karageorgopoulos, D. E. (2008). Comparison of SCImago journal rank indicator with journal impact factor. The Federation of American Societies for Experimental Biology Journal, 22(8), 2623-2628. Fassoulaki, A., Papilas, K., Paraskeva, A., & Patris, K. (2002). Impact factor bias and proposed adjustments for its determination. Acta Anaesthesiologica Scandinavica, 46, 902-905. http://dx.doi.org/10.1034/j.1399-6576.2002.460723.x Garfield, E. (1996). How can impact factors be improved? British Medical Journal, 313, 411-413. http://dx.doi.org/10.1136/bmj.313.7054.411 Garfield, E. (1998). Der impact faktor und seine richtige anwendung. Der Unfallchirurg, 101(6), 413-414. Garner, R., Lunin, L., & Baker, L. (1967). Three Drexel information science research studies. Madison: Drexel Press. Giles, C. L., Bollacker, K. D., & Lawrence, S. (1998). An automatic citation indexing system, Digital Libraries 98- Third ACM Conference on Digital Libraries, New York, 89-98. Greenwood, D. C. (2007). Reliability of journal impact factor rankings. BMC Medical Research Methodology, 7(48), 1-6. Hemmingsson, A., Mygind, T., Skjennald, A., & Edgren, J. (2002). Manipulation of impact factors by editors of scientific journals. American Journal of Roentgenology, 178(3), 767-767. http://dx.doi.org/10.2214/ajr.178.3.1780767 Joint Committee on Quantitative Assessment of Research. (2008, June 12). Citation Statistics, 1-26. Khan, & Hegde, (2009). Is impact factor true evaluation for ranking quality measure? Journal of Library & Information Technology, 29(3), 55-58. Kuo, W. & Rupe, J. (2007). R-impact: reliability-based citation impact factor. IEEE Transactions on Reliability 56(3), 366-367. http://dx.doi.org/10.1109/TR.2007.902789 Ma, N., Guan, J., & Zhao, Y. (2008). Bringing PageRank to the citation analysis. Information Processing and Management, 44(2), 800-810. http://dx.doi.org/10.1016/j.ipm.2007.06.006 Miguel, A., & Marti-Bonmati, L. (2002). Self-citation: comparison between Radiologia, European and radiology for 1997–1998. European Radiology, 12(1), 248-252. http://dx.doi.org/10.1007/s003300100894 Moed, H. F. (2005). Citation analysis in research evaluation. Information Science and Knowledge Management. Springer. Nigam, A., & Nigam, P. K. (2012). Citation index and impact factor. Indian Journal of Dermatology, Venereology, and Leprology, 78(4), 511-516. http://dx.doi.org/10.4103/0378-6323.98093 Offutt, J. (2008). The journal impact factor. Journal of software testing verification and reliability, 18(1). Palacios-Huerta, I., & Volij, O. (2004). The measurement of influence. , 72(3), 963-977. http://dx.doi.org/10.1111/j.1468-0262.2004.00519.x Rossner, M., Van Epps, H., & Hill, E. (2007). Show me the data. Journal of Biology, 179(6), 1091-1092. http://dx.doi.org/10.1083/jcb.200711140 Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. British Medical Journal, 314(7079), 498-502. http://dx.doi.org/10.1136/bmj.314.7079.497 Small, H. (1973). Co-citation in the : A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. http://dx.doi.org/10.1002/asi.4630240406 Smith, R. (1997). Journal accused of manipulating impact factor. British Medical Journal, 314(15), 461. http://dx.doi.org/10.1136/bmj.314.7079.461d The PLoS Medicine Editors. (2006). The impact factor game: It is time to find a better way to assess the scientific literature. PLoS Med., 3, 291. http://dx.doi.org/10.1371/journal.pmed.0030291

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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 . 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, , 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 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 . 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 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, , 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. 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 .

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

Applied Sciences 7 (4):198-202. doi: arXiv:1306.0727. • Farhadi, Maryam, Hadi Salehi, Mohamed Amin Embi, Masood Fooladi, Hadi Farhadi, Arezoo Aghaei Chadegani, and Nader Ale Ebrahim. 2013. "Contribution of Information and Communication Technology (ICT) in Country’S H-Index." Journal of Theoretical and Applied Information Technology 57 (1):122-127. doi: 10.5281/zenodo.7715. • Gargouri, Yassine, Chawki Hajjem, Vincent Larivière, Yves Gingras, Les Carr, Tim Brody, and Stevan Harnad. 2010. "Self-Selected or Mandated, Open Access Increases Citation Impact for Higher Quality Research." PLoS ONE 5 (10):e13636. doi: 10.1371/journal.pone.0013636. • Gholizadeh, Hossein, Hadi Salehi, Mohamed Amin Embi, Mahmoud Danaee, Seyed Mohammad Motahar, Nader Ale Ebrahim, Farid Habibi Tanha, and Noor Azuan Abu Osman. 2014. "Relationship among Economic Growth, Internet Usage and Publication Productivity: Comparison among ASEAN and World’s Best Countries." Modern 8 (2):160- 170. doi: 10.5539/mas.v8n2p160. • Ioannidis, J. P. A. 2010. "Is there a glass ceiling for highly cited scientists at the top of research universities?" Faseb Journal 24 (12):4635-4638. doi: 10.1096/fj.10-162974. • Krause, Kate. 2009. Increasing your Article's Citation Rates. Open Access Week. Accessed 28 May 2013. • Lai, Quirino, Tom Darius, and Jan Lerut. 2012. "The one hundred most frequently cited articles in the field of clinical liver transplantation." Transplant International 25 (6):e76-e77. • Lancho-Barrantes, Bárbara S., Vicente P. Guerrero-Bote, and Félix Moya-Anegón. 2010. "What lies behind the averages and significance of citation indicators in different disciplines?" Journal of Information Science 36 (3):371-382. doi: 10.1177/0165551510366077. • Meho, Lokman I. 2007. "The rise and rise of citation analysis." Physics World 20:32–36. • Pislyakov, Vladimir, and Elena Shukshina. 2012. "Measuring Excellence in Russia: Highly Cited Papers, Leading Institutions, Patterns of National and International Collaboration." Proceedings of STI 2012, Montréal. • Sarli, Cathy, and Kristi Holmes. 2011. "Strategies for Enhancing the Impact of Research." Washington University School of Medicine, Accessed 9 May. https://becker.wustl.edu/impact- assessment/strategies. • Swan, Alma. 2010. • Tang, Rong, and Martin A. Safer. 2008. "Author-rated importance of cited references in biology and psychology publications." Journal of Documentation 64 (2):246-272. doi: 10.1108/00220410810858047. • Uzun, Ali. 2006. "Statistical relationship of some basic bibliometric indicators in scientometrics research." International Workshop on Webometrics, Informetrics and Scientometrics & Seventh COLLNET Meeting, Nancy (France), May 10 - 12, 2006. • Vanclay, Jerome K. 2013. "Factors affecting citation rates in environmental science." Journal of Informetrics 7 (2):265-271. doi: http://dx.doi.org/10.1016/j.joi.2012.11.009. • Webster, Gregory D, Peter K Jonason, and Tatiana Orozco Schember. 2009. "Hot topics and popular papers in evolutionary psychology: Analyses of title words and citation counts in Evolution and Human Behavior, 1979–2008." Evolutionary Psychology 7 (3):348-362.

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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 , 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 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 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 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 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 (8 universities) or (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 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 and Information Technology. Malaysian Journal of Library & Information Science, 6, 1-23. Hart, R. L. (2000). Co-authorship in the academic library literature: A 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 , 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 . 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 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 (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 35 to 91 Biology -5 to 36 Electrical engineering 51 Computer science 157 Political science 86 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 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 (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 (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 . 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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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, 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, 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 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 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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and Citation Impact. International Education Studies, 7(4), 120-125. http://dx.doi.org/10.5539/ies.v7n4p120 Ale Ebrahim, N., Salehi, H., Embi, M. A., Habibi Tanha, F., Gholizadeh, H., Motahar, S. M., & Ordi, A. (2013). Effective Strategies for Increasing Citation Frequency. International Education Studies, 6(11), 93-99. http://dx.doi.org/10.5539/ies.v6n11p93 Amancio, D. R., Oliveira Jr, O. N., & da Fontoura Costa, L. (2012). Three-feature model to reproduce the topology of citation networks and the effects from authors’ visibility on their h-index. Journal of Informetrics, 6(3), 427-434. http://dx.doi.org/10.1016/j.joi.2012.02.005 Antelman, K. (2004). Do open-access articles have a greater research impact? College & Research Libraries, 65(5), 372-382. http://dx.doi.org/10.5860/crl.65.5.372 Bakkalbasi, N., Bauer, K., Glover, J., & Wang, L. (2006). Three options for citation tracking: Google Scholar, Scopus and Web of Science. Biomedical Digital Libraries, 3(1), 7. http://dx.doi.org/10.1186/1742-5581-3-7 Bensman, S. (2011). Anne-Wil Harzing: The publish or perish book: Your guide to effective and responsible citation analysis. Scientometrics, 88(1), 339-342. http://dx.doi.org/10.1007/s11192-011-0388-8 Bornmann, L., Marx, W., Schier, H., Rahm, E., Thor, A., & Daniel, H. D. (2009). Convergent validity of bibliometric Google Scholar data in the field of chemistry-Citation counts for papers that were accepted by International Edition or rejected but published elsewhere, using Google Scholar, Science Citation Index, Scopus, and Chemical Abstracts. Journal of Informetrics, 3(1), 27-35. http://dx.doi.org/10.1016/j.joi.2008.11.r001 Cabezas-Clavijo, A., & Delgado-Lopez-Cozar, E. (2013). Google Scholar and the h-index in biomedicine: The popularization of bibliometric assessment. Medicina Intensiva, 37(5), 343-354. http://dx.doi.org/10.1016/j.medin.2013.01.008 Craig, I. D., Plume, A. M., McVeigh, M. E., Pringle, J., & Amin, M. (2007). Do open access articles have greater citation impact?: A critical review of the literature. Journal of Informetrics, 1(3), 239-248. http://dx.doi.org/10.1016/j.joi.2007.04.001 Egghe, L., Guns, R., & Rousseau, R. (2013). Measuring co-authors' contribution to an article's visibility. Scientometrics, 95(1), 55-67. http://dx.doi.org/10.1007/s11192-012-0832-4 Ertürk, K., & Şengül, G. (2012). Self Archiving in Atılım University. In S. Kurbanoğlu, U. Al, P. Erdoğan, Y. Tonta, & N. Uçak (Eds.), E-Science and Information Management (Vol. 317, pp. 79-86): Springer Berlin Heidelberg. http://dx.doi.org/10.1007/978-3-642-33299-9_11 Fooladi, M., Salehi, H., Yunus, M. M., Farhadi, M., Aghaei Chadegani, A., Farhadi, H., & Ale Ebrahim, N. (2013). Do Criticisms Overcome the Praises of Journal Impact Factor? Asian Social Science, 9(5), 176-182. http://dx.doi.org/10.5539/ass.v9n5p176 Garfield, E. (1972). Citation analysis as a tool in journal evaluation. Science, 178, 471-479. http://dx.doi.org/10.1126/science.178.4060.471 Hardy, R., Oppenheim, C., Brody, T., & Hitchcock, S. (2005). Open Access Citation Information. Hooper, S. L. (2012). Citations: not all measures are equal. Nature, 483(7387), 36-36. http://dx.doi.org/10.1038/483036c Jacso, P. (2012). Google Scholar Metrics for Publications The software and content features of a new open access bibliometric service. Online Information Review, 36(4), 604-619. http://dx.doi.org/10.1108/14684521211254121 Kear, R., & Colbert-Lewis, D. (2011). Citation searching and bibliometric measures: Resources for ranking and tracking. College & Research Libraries News, 72(8), 470-474. Kousha, K., & Thelwall, M. (2007). Google Scholar citations and Google Web-URL citations: A multi-discipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055-1065. http://dx.doi.org/10.1002/asi.v58:7 Larsen, P. O., & von Ins, M. (2010). The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics, 84(3), 575-603. http://dx.doi.org/10.1007/s11192-010-0202-z Lawrence, S. (2001a). Free online availability substantially increases a paper's impact. Nature, 411(6837), 521-521. http://dx.doi.org/10.1038/35079151

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Lawrence, S. (2001b). Online or invisible. Nature, 411(6837), 521. http://dx.doi.org/10.1038/35079151 McCabe, M. J., & Snyder, C. M. (2013). Does Online Availability Increase Citations? Theory and Evidence from a Panel of Economics and Business Journals: SSRN working paper. Orduña-Malea, E., & Delgado López-Cózar, E. (2014). Google Scholar Metrics evolution: an analysis according to languages. Scientometrics, 98(3), 2353-2367. http://dx.doi.org/10.1007/s11192-013-1164-8 Pauly, D., & Stergiou, K. I. (2005). Equivalence of results from two citation analyses: Thomson ISI’s Citation Index and Google’s Scholar service. Ethics in Science and Environmental Politics, 5, 33-35. Rotich, D. C., & Musakali, J. J. (2013). Publish or Perish: Remaining Academically Relevant and Visible In the Global Academic Scene through Scholarly Publishing. Paper presented at the Conference and Programme Chairs, . Solomon, D. J., Laakso, M., & Björk, B.-C. (2013). A longitudinal comparison of citation rates and growth among open access journals. Journal of Informetrics, 7(3), 642-650. http://dx.doi.org/10.1007/s11192-013-1164-8 The Thomson Corporation. (2013). Essential Science Indicators, Product Overview. Retrieved from http://esi.webofknowledge.com/help//h_whatis.htm Yue, W. P., & Wilson, C. S. (2004). Measuring the citation impact of research journals in clinical neurology: A structural equation modelling analysis. Scientometrics, 60(3), 317-332. http://dx.doi.org/10.1023/B:SCIE.0000034377.93437.18 Zhang, C.-T. (2009). The e-Index, Complementing the h-Index for Excess Citations. PLoS ONE, 4(5), e5429. http://dx.doi.org/10.1371/journal.pone.0005429 Zheng, J., Zhao, Z. Y., Zhang, X., Chen, D. Z., Huang, M. H., Lei, X. P., … Zhao, Y. H. (2012). International scientific and technological collaboration of China from 2004 to 2008: a perspective from paper and analysis. Scientometrics 91(1), 65-80. http://dx.doi.org/10.1007/s11192-011-0529-0

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 Prevention in

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 . 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 proceedings, books, journals or magazines (Hockenos, 2013). Even though getting a paper published is very important, the 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 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 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 , 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 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 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.

(1)

According to a research in 2011 (Patel et al., 2011), the result of translated text from a western language into another western, back to the source language will not make many changes. So, it is suggested to translate it

58 www.ccsenet.org/ies International Education Studies Vol. 7, No. 7; 2014 across the eastern and western languages and at the end back to the source language. For example, consider that there is a text in English. It is suggested to translate it into an eastern language (e.g. Chinese), and then from the eastern language (e.g. Chinese) into a western language (e.g. French), then repeating this procedure by translating from western into eastern languages, and finally back into the original language which is English in this example. The resulted text would be quite different with the original text. Despite being an easy to use method, there are a few cautions that we should be aware of them. Firstly, the generated language might be grammatically incorrect and it should be edited. Sometimes editing a sentence is easier than paraphrasing the whole sentence or paragraph. Secondly, editing incorrect sentences takes a long time, and it is not as easy and fast as it looks like, but it helps a lot. 3.2.6 Periodic Self-Reading Once the paper is written, the authors should not leave or submit it. If they read what they have just written, they can only find the grammatical mistakes or if a word is missing in the text. Further failures will not be recovered in that stage. But if the authors read the text a few days later, they can cover more failures such as dim sections, or even having more new sentences written in order to extend the paper. Self-reading the paper periodically can fix the mistakes, improve and increase the quality of the writing. In spite of the benefits of having a periodic self-reading, over-reading a paper can cause obsessions. 3.2.7 Quoting There are some cases that the authors find themselves inevitable to write the exact words as the original text. For example, it is a famous saying, a scientific abbreviation or a known expression. In these cases, using quotation is a solution. Quoting someone else words is always ignored and not being considered as plagiarism. Quoting can be from a few words to hundreds. This technique is simply putting the text into two quotation marks and referencing the source at the end of the expression. For example, “A person who never made a mistake never tried anything new”—Albert Einstein. Besides the ethical solution of quoting, opening a quote in the beginning of the manuscript, and closing it at the end is an unethical and ineligible approach. 3.2.8 Realistic & Imaginative Context A solution (methodology) plays one of the most important roles for the whole journey of the research. The right solution drives the author to the destination, and the wrong one can lead him/her to nowhere. Imagination is the first place where the new ideas are born. A researcher tries to use his imaginations and to lead them to be realistic, and respect them by writing, feeding, raising, improving, and at the end publishing them. Although an imagination might be funny and stupid at the first sight, it has the potential of opening a new door toward the problem solving. Writing of imagination is usually and basically plagiarism-free, because they are purely the authors’ properties. 4. Efficiency Evaluation of Plagiarism Prevention Techniques In this section, the above ethical techniques are evaluated and the results are compared to check how effective they are in decrement of plagiarism and similarity at academic writings. A group of 16 individual students from the National University of Malaysia (Bangi, Malaysia) and Erican Language Centre (Kajang, Malaysia) has been selected. Five texts consist of 400-500 words were chosen from Wikipedia to have a primary 95% - 100% similarity index in Turnitin software. In the first stage, each of those above ethical techniques was taught to two students, and they were requested to apply that specific technique on all those 5 texts from Wikipedia. Also the students were allowed to edit the text as long as the meaning is the same. At the end of the first stage, each of those ethical techniques was applied to all 5 texts by two students. And 80 texts were produced from a nearly 100% plagiarized texts (copied from Wikipedia). These 80 texts were checked for plagiarism by Turnitin system.

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100 90 80 70 60 50 40 30

Plagiarism 20 10 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Text 1 40 51 20 25 65 59 75 81 34 43 70 75 85 89 45 51 Text 2 37 39 25 30 70 64 75 85 30 35 71 76 88 86 48 48 Text 3 47 43 29 32 75 63 79 87 28 41 65 70 87 81 41 51 Text 4 51 49 35 28 72 61 83 79 25 42 72 70 82 85 56 53 Text 5 42 55 21 29 68 62 78 80 35 45 75 73 80 79 42 50 No.

Text 1 Text 2 Text 3 Text 4 Text 5

Figure 3. Similarity index of each text by each student

Each 2 students are trained with an ethical method. Students 1 & 2 are trained with method 1 (3.2.1), students 3 & 4 with method 2 (3.2.2) and respectively, until the last 2 students of 15 & 16 with method 8 (3.2.8). Figure 3 shows 80 values in percentage resulted by 16 students on 5 different texts. It shows that methods number 2 (Writing after a Few Days of Reading), 5 (Using Machine Translators) and 8 (Realistic and Imaginative Context) are the top 3 methods to avoid plagiarism in a text. To clarify the above result, each of the 2 values of each method in a paper is averaged, and it is illustrated in Figure 4.

100 90 80 70 60 50 40 30

Plagiarism 20 10 0 12345678 Text 1 45.5 22.5 62 78 38.5 72.5 87 48 Text 2 38 27.5 67 80 32.5 73.5 87 48 Text 3 45 30.5 69 83 34.5 67.5 84 46 Text 4 50 31.5 66.5 81 33.5 71 83.5 54.5 Text 5 48.5256579407479.546 Methods

Text 1 Text 2 Text 3 Text 4 Text 5

Figure 4. Average of each method on every single text

Figure 5 illustrates the average of the similarity index after applying each method on all 5 texts. Lower values show less similarity and plagiarism rate of each method on the texts.

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100 90 7, 84.2 4, 80.2 80 6, 71.7 70 3, 65.9 60 8, 48.5 50 1, 45.4 40 5, 35.8 Plagiarism 2, 27.4 30 20 10 0 12345678 Method #

Figure 5. Average of similarity index of each method on all texts

In Figures 4 and 5, the effectiveness of each method is illustrated. As it can be shown, the best method to avoid plagiarism went to method 2 (Writing after a Few Days of Reading). We also decided to measure how these techniques might affect on the texts by applying all together. Then, for the final stage, all 16 students were requested to apply their learnt methods together on the texts again. This time, they were applying their methods collaboratively at the same time on 5 texts. And at the end, we had 5 texts with 8 methods applied on by 16 students. These 5 papers were evaluated to check the similarity index. Table 1 shows the plagiarism rate of each text after applying all 8 methods. The similarity indices of these texts were reduced from nearly 100% to the average of 8.4%, which is a brilliant similarity index by using of 8 methods and different students.

Table 1. Plagiarism rates of all 5 texts by applying 8 methods Text # Plagiarism 1 7% 2 5% 3 9% 4 13% 5 8% Average 8.4%

5. Conclusion This paper started with the explanations of plagiarism issues and literary piracy in academic manuscripts. It also presented few unethical methods to warn the authors, students and librarians about them. Then it proposed ethical methods to avoid the plagiarism and to decrease the similarity rate in the academic writings. These ethical methods were all evaluated on 5 manuscripts by 16 students and checked by Turnitin. The results show that all those methods can reduce the similarity index significantly. The most effective method was the second approach, “Writing after a Few Days of Reading”. The hybrid of all these methods can make an almost plagiarism-free context down to the average of 8.4% of similarity index. This research can be used by researchers to improve the quality and originality of their academic writings. References Anderson, J. (2013). Breaking the BTK Killer's Cipher. Cryptologia, 37(3), 204-209.

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http://dx.doi.org/10.1080/01611194.2013.797047 Carrol, J. (2002). A handbook for detecting plagiarism in higher education. London: Oxford: The Oxford Centre for Staff and Learning Development, Oxford Brookes University. Dictionary.com. (2013). Plagiarism. Retrieved January 8, 2013, from http://dictionary.reference.com/browse/Plagiarism Ebbinghaus, H. (1913). Memory: A contribution to experimental psychology. Teachers college, Columbia University. Encyclopedia-Britannica. (2013). Plagiarism. Retrieved January 8, 2013, from http://dictionary.reference.com/browse/Plagiarism Gillam, L., Marinuzzi, J., & Ioannou, P. (2010). TurnItOff: Defeating plagiarism detection systems. Paper presented at the 11th Higher Education Academy-ICS Annual Conference, University of Durham, UK. Google. (2011). Google Translator. Retrieved April 20, 2011, from http://translate.google.com/ Hawley, C. S. (1984). The thieves of academe: Plagiarism in the university system. Improving college and university teaching, 32(1), 35-39. http://dx.doi.org/10.1080/00193089.1984.10533838. Hockenos, P. (2013). High-Profile Plagiarism Prompts Soul-Searching in German Universities. Chronicle of Higher Education. IEEE. (2011). Cross Check User Guide. Retrieved April 20, 2011, from http://www.ieee.org/documents/CrossCheck_User_Guide.pdf ISO. (2008). Document management–Portable document format Part 1: PDF 1.7 (Vol. ISO 32000-1:2008): International Organiztion for Standardization. iThenticate.com. (2011, 08/04/2011). CrossCheck. Retrieved April 20, 2011, from http://www.crossref.org/crosscheck/index.html Mehta, S., Meyer, D., & Murgai, P. (2013). Identifying and ranking networked biographies and referral paths corresponding to selected qualifications: US Patent 20,130,018,871. Patel, A., Bakhtiyari, K., & Taghavi, M. (2011). Evaluation of Cheating Detection Methods in Academic Writings. Library Hi Tech, 29(4), 623-640. http://dx.doi.org/10.1108/07378831111189732. Schein, M., & Paladugu, R. (2001). Redundant surgical publications: Tip of the iceberg? Surgery, 129(6), 655-661. http://dx.doi.org/10.1067/msy.2001.114549. SysTranSoft.com. (2011). SYSTRAN–Online Translation Software. Retrieved April 20, 2011, from http://www.systransoft.com/

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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Cite as: N. Ale Ebrahim, “How to Promote Your Article,” University of Malaya Research Bulletin, vol. 1, no. 1, 23 June, 2014. doi: 10.6084/m9.figshare.1083502

How to Promote Your Article

Nader Ale Ebrahim

Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Kuala Lumpur, Malaysia

Abstract:

Writing an article for online distribution in a way that maximized the chances of citation hits, is different from preparing one for print journals in some small, but important, respects. To be cited, articles have to be visible in an electronic environment. Therefore, 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”. Familiarity with the tools allows the researcher to increase his/her h-index in the short time.

Keywords: University ranking, Improve citation, Citation frequency, Research impact, Open access, h-index, Increase citations, Research tools

Inroduction:

This article provides a list of simple yet effective ways to promote your publications [1-3].

1. Use a unique name consistently throughout academic careers. 2. Use a standardized institutional affiliation and address, using no abbreviations 3. Repeat key phrases in the abstract while writing naturally. 4. Assign keyword terms to the manuscript 5. Optimize your article for Google Scholar and other academic search engines 6. Make a unique phrase that reflects author's research interest and use it throughout academic life 7. The article metadata should be correct (especially author and title). 8. Publish in journal with high impact factor 9. Self-archive articles 10. Keep your professional web pages and published lists up to date (Make an attractive homepage that is clear about what you are working on.) 11. Make your research easy to find, especially for online searchers 12. Publish in an Open Access (OA) journal 13. Deposit paper in Open Access repository 14. Publish with international authors 15. Write article collaboratively (Team-authored articles get cited more) Cite as: N. Ale Ebrahim, “How to Promote Your Article,” University of Malaya Research Bulletin, vol. 1, no. 1, 23 June, 2014. doi: 10.6084/m9.figshare.1083502 16. Use more references 17. Cite relevant thought influencers in your article (To be the best, cite the best). 18. Publish a longer paper 19. Publish papers with a Nobel laureates 20. Contribute to Wikipedia 21. Start blogging and share your blog post with target customers 22. Share your article on all your social media platforms 23. Interact with your peer connections through academic social media. 24. Keep track of all your international contacts 25. Follow the conversation on academic social media 26. When your paper is finally published, individually email the pdf to selected collaborators 27. Respond to comments. 28. Write a review paper 29. Avoid to select a question type of title 30. Sharing detailed research data 31. Publish across disciplines 32. Present a working paper 33. Publish your article in one of the journals everyone in your discipline reads 34. Add your latest published article’s link to your email signature 35. Take 50 photocopies of your best 1-2 papers to conferences, and leave them on the brochure desk as a handout. 36. Increase number of publications in peer-reviewed journals 37. After a conference takes the paper, correct it, extend it, and submit it to a journal. 38. Publish your work in a journal with the highest number of abstracting and indexing 39. Create a podcast describing the research project 40. Make an online CV Like ORCID or ResearcherID. 41. Publish tutorials papers 42. Follow-up the papers which cited your article 43. Use all “Enhancing Visibility and Impact” tools which are available on http://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim. Cite as: N. Ale Ebrahim, “How to Promote Your Article,” University of Malaya Research Bulletin, vol. 1, no. 1, 23 June, 2014. doi: 10.6084/m9.figshare.1083502

Source: http://contentmarketinginstitute.com/2011/03/blog-post-to-dos/

References

[1] N. Ale Ebrahim, H. Salehi, M. A. Embi, F. Habibi Tanha, H. Gholizadeh, S. M. Motahar, and A. Ordi, “Effective Strategies for Increasing Citation Frequency,” International Education Studies, vol. 6, no. 11, pp. 93-99, October 23, 2013.

[2] N. Ale Ebrahim, H. Salehi, M. A. Embi, F. Habibi Tanha, H. Gholizadeh, and S. M. Motahar, “Visibility and Citation Impact,” International Education Studies, vol. 7, no. 4, pp. 120-125, March 30, 2014.

[3] N. Ale Ebrahim, “Introduction to the Research Tools Mind Map,” Research World, vol. 10, no. 4, pp. 1-3, June 14, 2013.

Journal of Theoretical and Applied Information Technology 10th November 2013. Vol. 57 No.1 © 2005 - 2013 JATIT & LLS. All rights reserved.

ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 CONTRIBUTION OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) IN COUNTRY’S H- INDEX

1MARYAM FARHADI1*, HADI SALEHI2,3, MOHAMED AMIN EMBI2, MASOOD FOOLADI1, HADI FARHADI4, AREZOO AGHAEI CHADEGANI1, and NADER ALE EBRAHIM5

1Department of Accounting, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran 2Faculty of Education, Universiti Kebangsaan Malaysia (UKM), Bangi, 43600, Malaysia 3Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran 4School of Psychology and Human Development, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia (UKM), Malaysia 5Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia *Corresponding author: Maryam Farhadi, Email: [email protected]

ABSTRACT

The aim of this study is to examine the effect of Information and Communication Technology (ICT) development on country’s scientific ranking as measured by H-index. Moreover, this study applies ICT development sub-indices including ICT Use, ICT Access and ICT skill to find the distinct effect of these sub-indices on country’s H-index. To this purpose, required data for the panel of 14 Middle East countries over the period 1995 to 2009 is collected. Findings of the current study show that ICT development increases the H-index of the sample countries. The results also indicate that ICT Use and ICT Skill sub- indices positively contribute to higher H-index but the effect of ICT access on country’s H-index is not clear. Keywords: Information and Communication Technology (ICT) development, H-index, Middle East

1. INTRODUCTION However, ICT revolution and use of internet caused many structural changes [3]. As an In the last decades, technology has improved example, the United States productivity has been the life style and made easier the human revived during the late 1990s and the early 2000s activities through a new civilization. A new because of the ICT development [4]. There are revolution named Information and many studies introducing ICT as an important Communication Technology (ICT) has occurred element to facilitate economic growth in both in the present world [1]. ICT is defined as a developing and developed countries [5, 6]. In concept including information equipment as well addition, ICT has the potential to increase health as computers and software, auxiliary equipment systems through the designing new methods to that is connected to a computer (such as prevent and detect disease [7]. Other studies calculators, cash registers, photocopiers), confirm the dominant role of ICT in improving equipment, and instruments [2]. and modernizing the learning methods and In other words, ICT is a technology with the aim educational systems [8, 9]. The fast progress in of connecting computers and other technology in the last two decades has reduced communication equipment to gather, produce, the cost of ICTs. In this situation, it is easier for process, classify, manage, create, and distribute more people to use and enjoy the advantages of information. After the invention of machines in ICTs [10]. 1970s, industrial revolution has been started and machines have been used instead of handworks.

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Journal of Theoretical and Applied Information Technology 10th November 2013. Vol. 57 No.1 © 2005 - 2013 JATIT & LLS. All rights reserved.

ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 Although, the key role of ICT is approved in East countries, this study applies the regression different areas such as engineering [11], techniques with countries’ H-index as dependent education [12], health care [13], and economy variable and IDI as independent variable as [6], its effect on the degree of scientific ranking follows: of a country is yet unexplored. Therefore, the aim of this study is to explore the impact of ICT Hit=++αα 0 1 IDI it ε it (1) development as measured by ICT Development Index (IDI) on H-index as a proxy of scientific where α 'sare parameters to be estimated, H progress of a country. represents H-index, IDI stands for ICT development index and subscripts i and t show In addition, in order to shed more lights on th th ε this area, present study applies three components the i country in t year, respectively. is the of ICT development namely, ICT Skill, ICT Use error term which is assumed to be independent of and ICT Access to examine the distinct impact of IDI. Then, for more inspections, instead of IDI, these components on H-index as a proxy of which captures the composite effect of ICT scientific progress of a country. To this purpose, development, the current study uses three ICT this study applies a multiple regression analysis sub-indices including ICT Access, ICT Use and on a panel of 14 Middle East countries over the ICT Skill as explanatory variables. In this period 1995 to 2009. approach, we can test the distinct impact of each sub-index on the country’s H-index. The current study suggests the use of H-index Consequently, in this step of the analysis, provided by SCImago database [14] to measure following equation (Equation 2) is used: the scientific ranking of country. Based on Hirsch [15], a scientist or a group of scientists Hit=+++µµ 0 1 Access it µ 2 Use it µ 3 Skill it + δ it has H-index of h when h of their papers has at (2) least h citations. In other words, H-index is the number of articles with citation number higher or where µ 's are parameters to be estimated, H equal to h [15]. The H-index, which has been shows H-index and Access, Use and Skill stand invented by Hirsch in 2005, proposes a simple for ICT development sub-indices. Moreover, and apparently strong measure to compare the th th subscripts i and t indicate the i country in t scientific productivity and visibility of countries year, respectively, and δ is the error term which and institutions [16]. is assumed to be independent of Access, Use and H-index can measure the quantity and quality Skill variables. of papers simultaneously since the total number of published articles and the number of citations 2.2. Data and Measurement received by that articles are considered in the calculation of H-index. In addition, there are 2.2.1. ICT development many studies, which reveal the benefits of H- index as an appropriate and fair tool for The IDI, as an indicator of ICT development evaluating and ranking scientists, laboratories, is computed based on Principal Component scientific journals, universities, institutions and Analysis (PCA), which combines various countries [17, 18, 19, 20, 21]. indicators in order to make single value. For the first time, IDI is introduced in 2009 by Hirsch [15] proposes that H-index can be Measuring the Information Society [23] and used to appraise not only the scientists but also considers the degree of progress in ICTs in more countries and institutions. Hence, following than 150 countries. of this indicator Jacso [22], current study also uses H-index as a includes ICT Access showing infrastructure and proxy to measure scientific ranking of countries. access, ICT Skill referring to ICT skill or capability and ICT Use displaying the extent of 2. METHODOLOGY AND DATA using ICT. Required ICT data to compute the IDI is extracted from United Nations Educational 2.1. Empirical Model Scientific and Cultural Organization (UNESCO) and ITU. Nowadays, ICT is considered as an In order to examine the effect of ICT important factor in development of countries, development on scientific degree of 14 Middle which are moving towards knowledge or

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ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 information-based societies. It is assumed that these countries have experienced the process of ICT development to turn into an information society. Therefore, the ITU [23] suggests the three-phase model including ICT readiness (showing the degree of networked infrastructure and ICT access), ICT intensity (showing the degree of ICT use by the society) and ICT impact (showing the result of effective and efficient ICT use). IDI is a composite indicator including ICT access, ICT skill and ICT use. Each sub-index is composed of a number of Figure 1. Three Stages In The Evolution Towards An indicators explained as follow: Information Society (Source: ITU [25]) 1. Access Sub-Index shows the availability of 2.2.2. h-index ICT infrastructure and individuals’ access to basic ICTs. This sub-index has five indicators Different sources report the H-index. Some of including fixed telephone lines per 100 these sources are subscription-based sources inhabitants, mobile cellular telephone such as Scopus and Web of Science (WoS) and subscriptions per 100 inhabitants, some of them are free access sources such as international Internet bandwidth (bit/s) per Harzing's Publish or Perish program on the basis Internet user, proportion of households with a of Google Scholar entries. It should be noted that computer and proportion of households with these sources report different H-index for the Internet access at home. same institution, country and scholar because of their different coverage. For example, WoS 2. Use Sub-Index shows the real degree of ICTs covers a high range of published journals while it strength of use based on the available does not cover high impact conferences. infrastructure and access indicators. Although, the coverage of publications in Scopus Considering the limitation on data for ICT is poor prior to 1996, it covers conferences Use at the global scale and results of the properly. Documents in Google Scholar receive PCA, this subgroup has three indicators more citations in comparison with those in including Internet users per 100 inhabitants, Scopus and WoS. It is argued that Google fixed broadband Internet subscribers per 100 Scholar has the most coverage of journals and inhabitants and mobile broadband conferences particularly those published after subscriptions per 100 inhabitants. 1990 [26]. 3. Skill Sub-Index captures the degree of ICT The current study collects the required data skill in different countries. Since the required on H-index for the panel of 14 Middle East data for many developing countries are not countries over the period 1995 to 2009, from collected, an appropriate indicator can be the SCImago database [14]. The SCImago Journal degree of literacy and education. Especially and Country Rank website presents the journal in developing countries, the poor level of and country scientific indicators based on the education is a major obstacle to the effective information included in the Scopus database. use of internet and computers. With the inclusion of ICT in school courses, school 3. FINDING AND DISCUSSION attendance may offer an appropriate indicator for students’ exposure to the Internet or In this section, we first estimate Equation 1, computers. Therefore, this subgroup has three which explores the impact of ICT development indicators including, adult literacy rate, on H-index as a proxy of country’s scientific secondary gross enrolment ratio and tertiary ranking. Then, applying the three ICT sub- gross enrolment ratio. The required data for indices, this study presents the estimation results this subgroup are extracted from the for Equation 2, which is corresponding to the UNESCO Institute for Statistics (UIS) [24]. effect of ICT development sub-indices on H- index.

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ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 Table 1. Regression Results For The Impact Of ICT Development On H-Index Dependent Variable: H-index Variables Coefficient Standard Error T IDI 31.69 4.67*** 6.79 Constant 24.16 13.35* 1.81 = 210 F(1, 208) = 46.07*** R-squared = 0.1813 Adjusted R-squared = 0.1774 Note: ***, ** and * denote statistically significant at 1%, 5% and 10%, respectively. Standard errors are heteroskedasticity consistent.

Table 1 shows the effect of ICT development sample countries. The estimated coefficients on each country’s H-index. The estimation show a positive and significant effect of ICT Use results indicate a positive and significant effect and ICT Skill on country’s scientific ranking. of ICT development on country’s scientific This finding asserts that increasing the ranking as proxied by H-index. This finding application of ICTs and improving the ICT skills suggests that suitable policies aim at improving might significantly increase the scientific degree the level of ICT development can significantly of a country. The results also indicate that if a increase the scientific rankings of a country. The country increases the ICT Use and ICT Skill results also assert that if a country raises its ICT indices by one unit, its H-index will raise by 133 development index by one unit, the H-index will and 407 units, respectively. As can be seen in increase by 31 units. As can be seen in Table 1, Table 2, the coefficient explaining the effect of the adjusted R-squared is equal to 0.1774, which ICT Access on H-index is negative but not means that ICT development can explain 17.74% significant. Finally, based on the adjusted R- of variations in country’s H-index. squared, ICT sub indices can explain 27.11% of variations in country’s H-index. Table 2 exhibits the impact of ICT development sub-indices on H-index of 14 Table 2. Regression Results For The Impact Of ICT Development Sub-Indices On H-Index

Dependent Variable: H-index

Variables Coefficient Standard Error t ICT Access -25.46 49.80 -0.51 ICT Use 133.23 64.64** 2.06 ICT Skill 407.28 64.86*** 6.28 Constant -151.10 35.82*** -4.22 Observation = 210 F(3, 206) = 26.91*** R-squared = 0.2816 Adjusted R-squared = 0.2711 Note: ***, ** and * denote statistically significant at 1%, 5% and 10%, respectively. Standard errors are heteroskedasticity consistent.

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ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4. CONCLUSION AND IMPLICATIONS [7] Haridasan, K., Rangarajan, S. and Pirio, G. “Re-Inventing Health Care Training In The This paper focuses on examining the effect of Developing World: The Case For Satellite ICT development on the scientific degree of 14 Applications In Rural Environments”, Online Middle East countries as proxied by H-index. The Journal of Space Communication, 14(Winter), results of the regression analysis show that ICT 2009. development has a positive and significant effect [8] Sharma, A., Gandhar, K., Sharma, S. and on the H-index of these countries. Moreover, Seema, S. “Role of ICT in the Process of applying the ICT development sub-indices, this Teaching and Learning”, Journal of Education study finds that ICT Use and ICT Skill might have and Practice, Vol. 2, No. 5, 2011, pp. 1-6. positive effects on H-index but the effect of ICT [9] Sangrà, A. and González-Sanmamed, M. “The Access is not clear since its corresponding role of information and communication coefficient is not statistically significant. technologies in improving teaching and Therefore, we can conclude that ICT induces learning processes in primary and secondary outcomes that leads to higher H-index values and schools”, Journal of Asynchronous Learning raises the scientific level of sample countries. In Networks, Vol. 15, No. 4, 2011, pp. 47-59. this situation, policy makers should aim [10] International Telecommunication Union, increasing the level of ICT development through “Measuring the information society: The ICT increasing its indicators including fixed and Development Index”, Place des Nations CH- mobile telephone lines, international Internet 1211 Geneva Switzerland, 2011. bandwidth, proportion of households with a [11] C.C. Aldea, A.D. Popescu, A. Draghici computer and Internet access, Internet users, fixed and G. Draghici, “ICT Tools Functionalities and mobile broadband Internet subscribers, adult Analysis for the Decision Making Process of literacy rate, secondary and tertiary gross Their Implementation in Virtual Engineering enrolment ratios. Teams”, Procedia Technology, Vol. 5, 2012, pp. 649-658. [12] F. Scheuermann and F. Pedró, REFERENCES: “Assessing the effects of ICT in education: Indicators, criteria and benchmarks for [1] R.J. Gordon, “Does the ‘New Economy’ international comparisons”, Luxembourg: measure up to the great inventions of the Publications Office of the , past?”, Journal of Economic Perspectives, 2009. Vol. 14, No. 4, 2000, pp. 49-74. [13] A.R. Bakker, “Health care and ICT, [2] S.D. Oliner and D.E. Sichel, “Computers and partnership is a must”, International Journal of output growth revisited: How big is the Medical Informatics, Vol. 66, No. 1-3, 2002, puzzle?”, Brookings Papers on Economic pp. 51-57. Activity, Vol. 2, 1994, pp. 273-334. [14] SCImago, SJR — SCImago Journal & [3] S.S. Cohen, J.B. Delong and J. Zysman, Country Rank, 2007, Retrieved March 11, “Tools for thought: What is new and important 2013, from http://www.scimagojr.com about the E-economy?”, BRIE Working Paper, [15] J.E. Hirsch, “An index to quantify an No. 138, 2000. individual’s scientific research output”, [4] D.W. Jorgenson and K.J. Stiroh, “Information Proceedings of the National Academy of technology and growth”, American Economic Science, Vol. 102, No. 46, 2005, pp. 16569- review Papers and Proceedings, Vol. 89, No. 16572. 2, 1999, pp. 109-115. [16] J.F. Molinari and A. Molinari, “A new [5] Gruber, H. and Koutroumpis, P. “Mobile methodology for ranking scientific telecommunications and the impact on institutions”, Scientometrics, Vol. 75, No. 1, economic development”, Economic Policy, 2008, pp. 163-174. Vol. 26, No. 67, 2011, pp. 387-426. [17] P. Ball, “Index aims for fair ranking of [6] Farhadi, M., Ismail, R. and Fooladi, M. scientists”, Nature, Vol. 436, No. 900, 2005. “Information and Communication Technology doi:10.1038/436900a. Use and Economic Growth”, PLoS ONE, Vol. [18] G. Saad, “Exploring the h-index at the 7, No. 11, 2012, e48903. author and journal levels using bibliometric doi:10.1371/journal.pone.0048903 data of productive consumer scholars and business-related journals respectively”,

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ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 Scientometrics, Vol. 69, No. 1, 2006, pp. 117- 120. [19] W. Glanzel, “On the h-index - A mathematical approach to a new measure of publication activity and citation impact”, Scientometrics, Vol. 67, No. 2, 2006, pp. 315- 321. [20] T. Braun, W. Glanzel and A. Schubert, “A Hirsch-type index for journals Scientometrics, Vol. 69, No. 1, 2006, pp. 169-173. [21] K.M. Mclntyre, L. Hawkes, A. Waret- Szkuta, S. Morand and M. Baylis, “The H- Index as a Quantitative Indicator of the Relative Impact of Human Diseases”, PLoS ONE, Vol. 6, No. 5, 2011. e19558. doi:10.1371/journal.pone.0019558. [22] P. Jacso, “The h-index for countries in Web of Science and Scopus”, Online Information Review, Vol. 33, No. 4, 2009, pp. 831-837. [23] International Telecommunication Union, “Measuring the information society: The ICT Development Index”, Place des Nations CH- 1211 Geneva Switzerland, 2009. [24] UNESCO Institute for Statistics (UIS), 2011. http://www.uis.unesco.org. [25] International Telecommunication Union, “Measuring the information society: The ICT Development Index”, Place des Nations CH- 1211 Geneva Switzerland, 2010. [26] L.I. Meho and K. Yang, “Impact of data sources on citation counts and rankings of LIS faculty: Web of science versus scopus and google scholar”, Journal of the American Society for Information Science and Technology, Vol. 58, No. 13, 2007, pp. 2105– 2125.

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From the SelectedWorks of Nader Ale Ebrahim

December 2014 Optimize Your Article for Search Engine

Contact Start Your Own Notify Me Author SelectedWorks of New Work

Available at: http://works.bepress.com/aleebrahim/93 Preprint version of: N. Ale Ebrahim, “Optimize Your Article for Search Engine,” University of Malaya Research Bulletin, vol. 2, no. 1, 23 December, 2014.

Optimize Your Article for Search Engine

Nader Ale Ebrahim

Research Support Unit, Centre of Research Services, IPPP, University of Malaya

Abstract

This article provides guidelines on how to optimize scholarly literature for academic search engines like Google Scholar, in order to increase the article visibility and citations.

Introduction

Academic search engine optimization (ASEO) is a debatable topic [1]. Some researchers believe “Designing scientific articles just for SEO would undermine the credibility and definitively the quality of the articles and the research presented” [2]. On the other hand, the inclusion of the articles in the index improves the ability to make their articles available to the academic community [3]. “If you want your paper be "accepted" by a search engine you have to accept the search engines style guidelines” [2]. The literature indicates a significant and positive relationship between both citation in Google Scholar and ISI Web of Science with the number of versions [4]. Increasing the visibility means rises the number of versions of an article and vice versa [5]. The number of “versions” will be shown in a Google Scholar search result. Figure 1 shows 33 different versions of an article entitled “Virtual Teams: a Literature Review [6]” and number of citations.

Figure 1 The number of “versions” in the Google Scholar search result

Discussion

There are several techniques that you can optimize your article for search engines:

1. Usage of strong keywords and synonyms; Once the keywords are chosen, they need to be mentioned in the right places: in the title, and as often as possible in the abstract and the body of the text (but, of course, not so often as to annoy readers).[1],

Preprint version of: N. Ale Ebrahim, “Optimize Your Article for Search Engine,” University of Malaya Research Bulletin, vol. 2, no. 1, 23 December, 2014.

2. The completeness of documents metadata; When documents are converted to PDF, all metadata should be correct (especially author and title). Some search engines use PDF metadata to identify the file or to display information about the article on the search results page. It may also be beneficial to give a meaningful file name to each article [1]. 3. Write a good and short title [7]; 4. Consistent spelling of authors’ first and last names; 5. Refer to reliable resources with high citations; 6. Select a proper journal; and 7. Deposit your paper in different repository; are some examples [8].

Google Scholar as one of the academic search engine indexes scholarly material from proprietary sources such as subscription and open access journals, conference proceedings and white papers. Google Scholar weighted heavily the title, the Journal name and author names also multiple versions of an article, (e.g. on your home page and in UM Research Repository) [1] (please refer to http://authorservices.wiley.com/bauthor/seo.asp [7] for more detail).

Beel, Gipp and Wilde [1] believe that academic search engine optimization (ASEO) should be a common procedure for researchers, similar to, selecting an appropriate journal for publication. ASEO should not be seen as a guide on how to cheat academic search engines. Rather, it is about helping academic search engines to understand the content of research papers and, thus, about how making this content more widely and easily available.

References

[1] J. Beel, B. Gipp, and E. Wilde, “Academic Search Engine Optimization (ASEO),” Journal of Scholarly Publishing, vol. 41, no. 2, pp. 176-190, 01/01/, 2010.

[2] http://blog.webometrics.org.uk. "Academic Search Engine Optimization: An inevitable evil?," 19 September, 2014; http://blog.webometrics.org.uk/2010/03/academic-search- engine-optimization-an-inevitable-evil/.

[3] J. Beel, and B. Gipp, “On the robustness of google scholar against spam,” in Proceedings of the 21st ACM conference on Hypertext and hypermedia (HT’10), Toronto, Ontario, , 2010, pp. 297-298.

[4] N. Ale Ebrahim, H. Salehi, M. A. Embi, M. Danaee, M. Mohammadjafari, A. Zavvari, M. Shakiba, and M. Shahbazi-Moghadam, “Equality of Google Scholar with Web of Science Citations: Case of Malaysian Engineering Highly Cited Papers,” Modern Applied Science, vol. 8, no. 5, pp. 63-69, August 6, 2014.

[5] N. Ale Ebrahim, H. Salehi, M. A. Embi, F. Habibi Tanha, H. Gholizadeh, and S. M. Motahar, “Visibility and Citation Impact,” International Education Studies, vol. 7, no. 4, pp. 120-125, March 30, 2014.

Preprint version of: N. Ale Ebrahim, “Optimize Your Article for Search Engine,” University of Malaya Research Bulletin, vol. 2, no. 1, 23 December, 2014.

[6] N. Ale Ebrahim, S. Ahmed, and Z. Taha, “Virtual Teams: a Literature Review,” Australian Journal of Basic and Applied Sciences, vol. 3, no. 3, pp. 2653-2669, 2009.

[7] "Optimizing Your Article for Search Engines," 23 September; http://authorservices.wiley.com/bauthor/seo.asp.

[8] N. Ale Ebrahim, H. Salehi, M. A. Embi, F. Habibi Tanha, H. Gholizadeh, S. M. Motahar, and A. Ordi, “Effective Strategies for Increasing Citation Frequency,” International Education Studies, vol. 6, no. 11, pp. 93-99, October 23, 2013.

Modern Applied Science; Vol. 8, No. 2; 2014 ISSN 1913-1844 E-ISSN 1913-1852 Published by Canadian Center of Science and Education Relationship among Economic Growth, Internet Usage and Publication Productivity: Comparison among ASEAN and World’s Best Countries

Hossein Gholizadeh1, Hadi Salehi2, Mohamed Amin Embi3, Mahmoud Danaee4, Seyed Mohammad Motahar5, Nader Ale Ebrahim6, Farid Habibi Tanha7 & 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 Faculty of Information Science and Technology,Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia 6 Research Support Unit, Centre of Research Services, Institute of Research Management and Monitoring (IPPP), University of Malaya, Malaysia 7 Department of Financial Sciences, University of Economic Sciences, Tehran, 1593656311, Iran Correspondence: Hadi Salehi, Faculty of Literature and Humanities, Najafabad Branch, Islamic Azad University, Najafabad, Isfahan, Iran. Tel: 98-913-104-1419. E-mail: [email protected]

Received: January 5, 2014 Accepted: February 7, 2014 Online Published: March 14, 2014 doi:10.5539/mas.v8n2p160 URL: http://dx.doi.org/10.5539/mas.v8n2p160

Abstract Publication productivity, as measured by the number of papers, has been regarded as one of the main indicators of reputation of countries and institutions. Nevertheless, the relationship among research publications, economic growth and World Wide Web in ASEAN countries is still unclear. The main intention of this study was to identify publication productivity among ASEAN and the world’s top ten countries in the last 16 years (1996-2011). This study also aimed at finding the relationship among publication, gross domestic product (GDP) and internet usage. Furthermore, the publication trend in the 10 first Malaysian universities was evaluated for the same periods. Scopus database was used to find the overall documents, overall citations, citations per document and international collaboration from 1996 to 2011 for each country. The World Bank database (World Data Bank) was used to collect the data for GDP and the number of internet users. Moreover, to evaluate 10 top Malaysian universities, the number of published articles, conferences, reviews, and letters for the same periods was collected. The results of this study showed significant differences among ASEAN and top 10 countries regarding publication productivity. Moreover, a positive and significant relationship was observed between indices, GDP and internet usage for these countries. Surprisingly, international collaboration had a significant and negative relationship with economic growth. Malaysia had fewer citations per document (7.64) and international collaboration (36.9%) among ASEAN countries. In conclusion, international collaboration between academic institutes and researchers is influenced by economic growth and access to internet in the countries. Furthermore, publication trends in ASEAN countries are promising. However, policy makers and science managers should try to find different ways to increase the quality of the research publication and to raise citation per document. Keywords: ASEAN countries, publication productivity, citation, internet user, collaboration, economic growth 1. Introduction The Association of Southeast Asian Nations, a geo-political and economic organization including ten countries located in Southeast Asia, was formed on 8 August 1967 by Indonesia, Malaysia, the Philippines, Singapore and Thailand. Since then, the membership has expanded to include Brunei, Burma (Myanmar), Cambodia, Laos, and Vietnam. One of the main aims of this association is accelerating economic growth (Sarel, 1997). The main tool for measuring a country's economy is Gross domestic product (GDP) that is the market value of all officially recognized final goods and services produced within a country in a given period of time.

160 www.ccsenet.org/mas Modern Applied Science Vol. 8, No. 2; 2014

Based on the literature, there is a relationship between economic growth and education and research publication. However, this relationship has not been well supported by realistic evidence (Jin & Jin, 2013; Nelson & Phelps, 1966; Lucas, 1988; Becker, 2009; Romer, 1990). The Researches by Mankiw et al. (1992) and Barro (1991) showed positive and significant effects of education on economic growth (Mankive et al., 1992; Barro, 1991). While the study by Bils and Klenow (2000) showed a possibility of reverse relationship between economic growth and education (Bils & Klenow, 2000). Moreover, in a recent research study, Jin and Jin (2013) indicated that publication productivity in different fields has dissimilar effects on economic growth. For instance, engineering and science have positive influences on economic growth in comparison with publication output in the field of social sciences (J. Jin & L. Jin, 2013). These days, publication productivity is a main task for researchers and students than in the past decades and the results of research should benefit the community (Zain et al., 2009). Based on new developmental principles, the number of knowledgeable employees could be increased by education (J. Jin & L. Jin, 2013). In addition, educated human resource would help the economy of the countries to grow faster. Moreover, it is widely observed that the world’s top productive research universities are in the highly developed countries such as the United States, the United Kingdom and Canada et al. Publication count is an indicator of research productivity and used to rank countries and universities (Liu & Cheng, 2005; Meho & Spurgin, 2005; Narin & Hamilton, 1996; Toutkoushian et al., 2003; Yazit & Zainab, 2007). It can also be used to determine authors’ productivity or the publication productivity of research groups (Liu & Cheng, 2005; Hart, 2000; Uzun, 2002; Gu & Zainab, 2001; Fox, 1983). The number of citations of previously published works is an indicator of its subsequent recognition and impact in a field of study. Reviewing articles that are frequently cited can provide information about the dominant areas of a discipline and also highlight the growth of particular fields. Furthermore, top-cited articles are often written by recognized experts who can offer insights into future directions of the discipline (Joynt & Leonard, 1980; Kelly et al., 2010; Lefaivre & O’Brien, 2011; Ponce & Lozano, 2010). Science Citation Index appeared in 1961 (following that, the Social Sciences Index was formulated in 1966). Using this index, it has been possible to answer questions such as “who publishes more articles” and “whose articles are being cited more often?” (Bar-Ilan, 2008; Heeks & Bailur, 2007; Meho & Yang, 2007). Hirsch developed the H-index (proposed by Jorge E. Hirsch, a physicist at UCSD, in 2005) as an alternative to traditional bibliometric measures. The H-index is a single bibliometric indicator that combines measures of productivity and impact into one. Hirsch argued that this combination reflects the impact of a publication on the corresponding scientific community (Braun et al., 2006; Schubert & Glänzel, 2007). 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 is also easily available and may be used for performance measures of scientists and for recruitment decisions (Aghaei et al., 2013; Ale Ebrahim et al., 2013; Brown & Gardner, 1985; Fooladi et al., 2013). In this study, we used the human development index (HDI) to find the top ten countries around the world. HDI is a composite statistic used to rank countries according to their development levels from “very high” to “low”. Countries are placed based on life expectancy, education, living standard, child welfare, health care, economic welfare, and population’s happiness. The main purpose of this study was to compare publication productivity among ASEAN and the world’s top ten countries from 1996 to 2011. In addition, we intended to find the relationship among publication productivity, gross domestic product (current US$), and internet usage. Finally, we aimed to investigate the publication trend of the 10 first Malaysian universities in the same periods. In this study, Scopus database (WWW.SCIMAGOJR.COM) was used since it is a primary full-text scientific database and offers about 20% more coverage than Web of Science (Anad & Sen, 1994). We hypothesized publications of academic papers as the research outputs have positive relationship with economic growth and the number of people with access to the worldwide network. Also, there is a significant difference between ASEAN publication productivity and the world’s top ten countries. 2. Method To identify publication productivity among ASEAN and the world’s top ten countries, Scopus database (SCImago) was used to compare overall documents and citations, cited documents, un-cited documents, self-citations, citations per document and international collaboration from 1996 to 2011. Moreover, the World Bank database (World Data Bank) was used to collect data for GDP and the number of internet users in these countries. To find the top ten countries around the world, we used the human development index (HDI) (Anad & Sen,

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1994). Moreover, to find the 10 first universities in Malaysia, we used the number of publications in Scopus database. Furthermore, to evaluate these universities, the number of published articles, conferences, reviews, and letters for the same periods was collected (1996-2011). 2.1 Data Analysis Prior to data analysis, normality test was done for the obtained data. The normality test revealed that all variables were not distributed normally. So, to answer the research questions, non parametric test was used. To evaluate the relationship among related items of publication, GDP and Internet usage, Spearman correlation coefficient test was applied. Moreover, to compare ASEAN countries with the top ten countries, Mann–Whitney U test was used. For evaluating the differences in number of documents, citations, self-citations, and citations per document among ASEAN countries, Kruskal-Wallis test was applied. The Kruskal-Wallis test is a nonparametric test that compares three or more unmatched groups and it is an extension of the Mann-Whitney U test to allow the comparison of more than two independent groups. 3. Results Based on human development index (HDI), the top ten countries around the world are listed as follow: Norway, Australia, USA, The , Germany, New Zealand, Ireland, Sweden, Switzerland, and Japan.

Table 1. Comparison between ASEAN countries and the 10 first countries around the world (publication productivity and country ranking)

Overall Documents Country ranking* Country (1996_2011) in Scopus publication H-Index Brunei 1064 133 140 Cambodia 1296 128 112 Indonesia 16139 63 57 Laos 853 141 135 Malaysia 75530 42 54 ASEAN Myanmar 906 139 146 Philippines 11326 70 56 Singapore 126881 32 30 Thailand 69637 43 39 Vietnam 13172 67 60 Norway 141143 31 21 Australia 592533 11 11 USA 6149455 1 1 Netherland 487784 14 8 TOP Germany 1581429 5 3 TEN New Zealand 114495 34 26 Ireland 91125 39 27 Sweden 337135 18 10 Switzerland 350253 17 9 Japan 1604017 4 6 * based on 238 countries in the world.

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Table 2. Kruskal Wallis test ASEAN countries Documents (Mean) SD Chi square* p.value Brunei 66.5 32.26 Cambodia 81 64.46 Indonesia 1008.688 663.05 Laos 53.3125 45.95 Malaysia 4720.625 5443.05 138 <0.01 Myanmar 56.625 41.21 Philippines 707.875 283.60 Singapore 7930.063 3940.70 Thailand 4352.313 2963.70 Vietnam 823.25 609.77 ASEAN countries Overall Citation (Mean) SD Chi square* p.value Brunei 484.0625 194.40 Cambodia 801.1875 614.26 Indonesia 7406.875 2823.18 Laos 478.1875 400.11 Malaysia 17601.19 5917.94 140.628 <0.01 Myanmar 428.0625 245.58 Philippines 7586.875 2320.56 Singapore 83167.75 32859.46 Thailand 33060.56 14704.18 Vietnam 6725.25 2789.02 ASEAN countries Self-citation (Mean) SD Chi square* p.value Brunei 43.4375 19.69 Cambodia 88.0625 72.82 Indonesia 857.4375 223.05 Laos 64.875 50.88 Malaysia 4345.5625 2483.06 142.822 <0.01 Myanmar 33.625 18.62 Philippines 866.0625 240.79 Singapore 11939.563 4584.45 Thailand 5831.5 2407.33 Vietnam 789.25 394.57 Citation per document ASEAN countries SD Chi square* p.value (Mean) Brunei 9.5081 5.37 Cambodia 12.2075 6.05 Indonesia 10.8456 5.18 Laos 11.8269 5.86 Malaysia 7.64 3.98 13.099 <0.01 Myanmar 11.5994 6.61 Philippines 12.9463 6.20 Singapore 12.5063 5.02 Thailand 10.6606 4.97 Vietnam 11.755 5.75 To investigate the differences among ASEAN countries, Kruskal Wallis test was applied. The results for all these indices showed that there were significant differences among these countries.

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As Tables 1 and 2 show, Singapore had the highest publications (126881 documents) and Laos had the lowest publications (853 documents) among ASEAN countries, while Malaysia ranking (with 75530 documents) was the second and the forty second among ASEAN and all the world countries, respectively (based on 238 countries in the world). Laos with 853 documents, and the publication ranking of 141 from 1996 to 2011 was the last country in this region. The USA was the first country in the world with the highest number of publications (6149455), H-index (1035) and overall citation (114546415) (see Table 1). Interestingly, Malaysia had the fewest citations per document (overall 7.64 cite per document) and the least collaboration with the world (37%) among ASEAN countries, whereas Laos had the most international collaboration (91%) in this region from 1996 to 2011 (see Figure 1 and Table 2). Furthermore, Malaysia and the United States had the highest self-citations with 24 % and 48% of all the citations that were relieved during that period among ASEAN and the top ten countries in the world (see Table 2 and Figure 1). Moreover, Malaysia had the least collaboration with the world in the last 16 years among ASEAN countries (1996-2011). As Table 2 shows, there are significant differences among the number of documents (H(2) = 138, p < 0.01), citations (H(2) = 140.62, p<0.01), self-citations (H(2) = 142.82, p<0.01), and citations per document (H(2) = 13.09, p<0.01) among ASEAN countries. Publication trends in ASEAN countries are promising in comparison with the top ten countries in the world (see Figure 2 and Table 3). There was a significant difference (p<0.01) between ASEAN and developed countries in number of documents, citable documents, citations (overall), self-citations, cited documents and research collaboration with the world. The rate of growth (Figure 2) in scientific publication was 24% in Cambodia (the highest in ASEAN), while Japan had 1.4% of growth in publication among these countries. To evaluate the relationship among related items of publication productivity and GDP and Internet usage, Spearman correlation coefficient was applied. Table 4 shows the correlation between indices and GDP and internet usage for ASEAN and the top ten countries separately. The results showed that there was a positive and significant relationship between GDP and the number of publications, citable documents, citation, self-citation and cited documents in ASEAN and the top ten countries in the world. However, one variable of international collaboration had a significant and negative relationship with GDP. Regarding the relationship between internet usage and all the variables, we found a significant and positive relationship in ASEAN countries except for the variable of international collaboration. However, there was a positive and significant (p<0.01) relationship between international collaboration and internet usage in the top ten countries. Prior to data analysis, normality test was conducted. The results revealed that all variables were not distributed normally so that we used Kruskal Wallis test, a non-parametric test, to examine publication productivity among the top ten Malaysian universities. Table 5 shows the mean and standard deviation for all types of publications among the top ten universities in Malaysia. The results of Kruskal Wallis test confirmed a significant difference among these universities. The institution with the first ranking in Malaysia was University of Malaya with 13105 publications from 1996 to 2011. The main field of research in this university was physics and astronomy with 15 % of all publications. et al.,Moreover, the result of Spearman correlation coefficient test showed a positive and significant relationship among the number of articles, conference papers, reviews and letters published during these years. Except the number of conference papers and letters, all other coefficients showed a strong relationship.

Table 3. Comparison between ASEAN and the top 10 countries for publication ASEAN TOP TEN COUNTRIES REGION Z P value Mean SD Mean SD Documents 1980.0 3453.7 71558.6 112595.0 -14.5 <0.01 Citable Documents 1859.9 3263.2 67369.3 104758.0 -14.518 <0.01 Citation (overall) 15774.0 27026.4 1190700.0 2256220.0 -14.9 <0.01 Self-citation 2485.9 4098.7 446135.2 1079040.0 -14.86 <0.01 Cited Documents 1277.8 2021.3 53836.4 84267.2 -14.748 <0.01 International 63.2 19.4 39.8 11.2 -10.206 <0.01 Collaboration To compare ASEAN countries and the top ten countries, Mann–Whitney U test was applied. The results showed significant differences for all indices

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Table 4. Spearman correlation coefficient REGION Index GDP Internet users Documents .853** .681** Citable Documents .854** .669** Citation (overall) .699** .511** ASEAN Self-citation .737** .534** Cited Documents .823** .646** % International Collaboration -.502** -.595** Documents .972** .201* Citable Documents .973** .199* Citation (overall) .757** -.224** TOP TEN Self-citation .780** -.165* Cited Documents .940** 0.121 % International Collaboration -.407** .557** **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed).

Table 5. Kruskl Wallis test was applied to examine publication productivity among the top ten Malaysian universities Article Conference Review Letter university Mean SD Mean SD Mean SD Mean SD University of Malaya 638 618 125 152 32 34 9 6 Universiti Sains Malaysia 570 609 131 160 19 24 4 4 Universiti Putra Malaysia 570 645 114 143 20 24 1 2 Universiti Kebangsaan Malaysia 416 509 188 287 14 20 2 3 Universiti Teknologi Malaysia 180 262 210 282 6 12 0 0 Universiti Teknologi MARA 103 153 150 281 4 7 0 1 International Islamic University 98 131 57 92 4 4 1 2 Malaysia Multimedia University 111 100 89 87 4 5 0 0 Universiti Teknologi Petronas 51 98 85 155 2 4 0 0 Universiti Malaysia Perlis 32 58 43 70 1 2 0 0 Chi square 84 22 73 90 P value <0.01 <0.01 <0.01 <0.01 Article Conference Review Conference .817** Review .894** .717** Letter .746** .488** .741** Spearman’s rho Correlation Coefficients

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A

B

C

D

Figure 1. Comparison among ASEAN and World's Best Countries regarding self citation (A); Citation per document (B); Un-cited documents (C); and International collaboration (D)

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A

B

C

Figure 2. Publication trend in ASEAN countries and World's Best Countries (A); Publication trend in Malaysia (B); Citation per document in Malaysia (C)

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4. Discussion and Conclusion In the current study, we made a comparison in Scopus database (SCImago) between ASEAN and the top 10 countries in the world regarding the relationship among economic growth, internet usage and publication productivity from 1996 to 2011. In addition, we made a comparison among the first 10 Malaysian universities. We used SJR, or SCImago Journal and Country Rank, as a measure of the scientific prestige of scholarly sources derived from the Google Page Rank™ algorithm. It attempts to even out the differences in citation practice in different subject fields and facilitate direct comparisons of sources. Furthermore, the SCImago Journal and Country Rank portal displays journal and country scientific indicators developed from the information contained in the Scopus® database. These indicators can be used to assess and analyze scientific domains. Publication productivity could be a good indicator of research output and used to rank countries, research institutes, or researchers in different fields (Liu & Cheng, 2005; Meho & Spurgin, 2005; Narin & Hamilton, 1996; Toutkoushian et al., 2003; Yazit & Zainab, 2007). The impact of a publication is assessed in terms of the number of citations that it has received in relation to other outputs in the journal (Yi et al., 2008). However, the number of times cited for an article is highly correlated with the length of time since its publication (Yi et al., 2008). There was a significant difference (p < 0.01) between citation per document (Yi et al., 2008; Calver & Bradley 2009) in ASEAN region (11.1 citation per document) and developed countries (17.7) (see Table 3). Malaysia got 31.5 % and 57.1 % less citation per document in comparison with the average of citation per document in ASEAN and top ten developed countries respectively. However, Malaysia had the highest self-citation in this region with almost 24 % of all citations which were received during this period. The United States had the highest number of publications as well as the highest self-citation (48%) in the world. Malaysia had the least collaboration with the world in comparison with other ASEAN countries and it could be one of the reasons for receiving less citation per document (Ale Ebrahim et al., 2013). Based on the literature, the mean value of citation per publication of collaborative papers was higher than that of single country publications. Publication trends in ASEAN countries with 10.1% growth are promising in comparison with the developed countries studied in (2.3%). Figure 2 represents the publication trend and the number of publications in Malaysia between 1996 and 2011. The number of publications increased dramatically after 2007 with 4783 to 18875 documents in 2011 (almost 4 times). However, the number of citations decreased rapidly from 5.09 to 0.47 citations per document during these years (11times). One of the main reasons for the increase in the number of publications in Malaysia could be focusing heavily on increasing the quality of research in its research universities such as University of Malaya. Malaysia spends 1% of its gross domestic product on development and research projects as determined in the 10th Malaysian Plan. Moreover, increasing the number of PhD students and changing their program from conventional dissertations to paper-based dissertations (Its requirement is publication in high peer-reviewed journals) could be another reason. The number of PhD students in Malaysia has increased 10 times in recent years (from about 4,000 students in 2002 to almost 40,000 in 2012). Table 5 shows the 10 first universities in Malaysia. The first top five universities have been chosen as research universities and received additional government funding. Two third (68.8 %) of all Malaysian publications have been produced by these research universities from 1996 to 2011. Chemistry, Physics, and Astronomy are the first fields regarding the number of publications and citations. Institutes that are working in these fields could publish more papers and get more citations. Malaysian universities mostly work on Engineering and Medicine in comparison with the top universities located in the top countries such as the United States. Our findings in this study showed a positive and significant relationship between GDP and the number of publications. This finding was similar to the previous researches that found positive and significant relationship between education and economic growth (Mankiw et al., 1992; Barro, 1991; Bils & Klenow, 2000). Moreover, a recent research study by Jin and Jin (2013) indicated that publication productivity in different fields has dissimilar effects on economic growth. Surprisingly, for one variable i.e., international collaboration, we found that there is a significant and negative relationship between international collaboration and the GDP in ASEAN countries, while the number of people with access to the World Wide Web in developed countries had significant and positive relationship with research collaboration projects. The countries with more economic growth like the United States had less research collaboration with other counties. Publication trends in ASEAN countries and especially in Malaysia (20.14 times increased) are encouraging in comparison with the top ten developed countries. Nevertheless, these publications could not get reasonable citations. Researchers, science managers, as well as policy makers should try to find different ways to increase citation per document (Ale Ebrahim et al., 2013) such as improving the quality and visibility of their researches

168 www.ccsenet.org/mas Modern Applied Science Vol. 8, No. 2; 2014 and invest more on research studies whose outputs lead to more rapid economic growth. Acknowledgements The authors wish to extend their best gratitude to Mrs. Elham Sadat Yahyavi, and Mrs. Rezvan ...... for their kind assistance in this project. References Aghaei Chadegani, A., Salehi, H., Md Yunus, 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 Ale Ebrahim, N., Salehi, H., Embi, M. A., Habibi, T. F., Gholizadeh, H., & Motahar, S. M. (2013). Effective Strategies for Increasing Citation Frequency. International Education Studies, 6, 93-99. http://dx.doi.org/10.5539/ies.v6n11p93 Anand, S., & Sen, A. (1994). Human development index: methodology and measurement. Human Development Report Office (HDRO), United Nations Development Programme (UNDP). Bar-Ilan, J. (2008). Which h-index?—A comparison of WoS, Scopus and Google Scholar. Scientometrics, 74(2), 257-271. http://dx.doi.org/10.1007/s11192-008-0216-y Barro, R. J. (1991). Economic growth in a cross section of countries. The quarterly journal of economics, 106(2), 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 Braun, T., Glänzel, W., & Schubert, A. (2006). A Hirsch-type index for journals. Scientometrics, 69(1), 169-173. http://dx.doi.org/10.1007/s11192-006-0147-4 Brown, L. D., & Gardner, J. C. (1985). Using citation analysis to assess the impact of journals and articles on contemporary (CAR). Journal of Accounting Research, 23(1), 84-109. http://dx.doi.org/10.2307/2490908 Calver, M. C., & Bradley, J. S. (2009). Should we use the mean citations per paper to summarise a journal’s impact or to rank journals in the same field? Scientometrics, 81(3), 611-615. http://dx.doi.org/10.1007/s11192-008-2229-y 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(2), 338-342. http://dx.doi.org/10.1096/fj.07-9492LSF Fooladi, M., Salehi, H., Md Yunus, M., Farhadi, M., Aghaei Chadegani, A., Farhadi, H., & Ale Ebrahim, N. (2013). Does Criticisms Overcome the Praises of Journal Impact Factor? Asian Social Science, 9(5), 176-182. http://dx.doi.org/10.5539/ass.v9n5p176 Fox, M. F. (1983). Publication productivity among scientists: A critical review. Social Studies of Science, 13(2), 285-305. http://dx.doi.org/10.1177/030631283013002005 Gu, Y., & Zainab, A. N. (2001). Publication productivity of Malaysian researchers in the field of Computer Science and Information Technology. Malaysian Journal of Library & Information Science, 6(1), 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(5), 339-345. http://dx.doi.org/10.1016/S0099-1333(00)00140-3 Heeks, R., & Bailur, S. (2007). Analyzing e-government research: Perspectives, , , methods, and practice. Government Information Quarterly, 24(2), 243-265. http://dx.doi.org/10.1016/j.giq.2006.06.005 Jin, J. C., & Jin, L. (2013). Research publications and economic growth: evidence from cross-country regressions. Applied Economics, 45(8), 983-990. http://dx.doi.org/10.1080/00036846.2011.613785 Joynt, R. L., & Leonard, J. A. (1980). Dantrolene sodium suspension in treatment of spastic cerebral palsy. Developmental Medicine & Child Neurology, 22(6), 755-767. http://dx.doi.org/10.1111/j.1469-8749.1980.tb03742.x

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Kelly, J. C., Glynn, R. W., O’Briain, D. E., Felle, P., & McCabe, J. P. (2010). The 100 classic papers of orthopaedic surgery a bibliometric analysis. Journal of Bone & Joint Surgery, British Volume, 92(10), 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(5), 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(2), 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(1), 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(2), 407-437. http://dx.doi.org/10.2307/2118477 Meho, L. I., & Spurgin, K. M. (2005). Ranking the research productivity of library and information science 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. http://dx.doi.org/10.1002/asi.20227 Meho, L. I., & Yang, K. (2007). Impact of data sources on citation counts and rankings of LIS faculty: Web of Science versus Scopus and Google Scholar. Journal of the American Society for Information Science and Technology, 58(13), 2105-2125. http://dx.doi.org/10.1002/asi.20677 Narin, F., & Hamilton, K. S. (1996). Bibliometric performance measures. Scientometrics, 36(3), 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, 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(2), 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. Schubert, A., & Glänzel, W. (2007). A systematic analysis of Hirsch-type indices for journals. Journal of Informetrics, 1(3), 179-184. http://dx.doi.org/10.1016/j.joi.2006.12.002 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(2), 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(3), 297-307. http://dx.doi.org/10.1023/A:1014864827441 Yazit, N., & Zainab, A. N. (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(1), 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(17), 16-26.

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Research World, Volume 10, 2013 Online Version

Article A10.4

Introduction to the Research Tools Mind Map

Nader Ale Ebrahim Research Support Unit, Centre of Research Services Institute of Research Management and Monitoring (IPPP) University of Malaya, Malaysia aleebrahim[at]siswa.um.edu.my

Published Online: June 14, 2013

With the increasing use of information and communications technology (ICT), researchers are able to use computer software tools to find, organise, manipulate, analyse, and share relevant information. However, there are hundreds of such tools to select from, for various research-related uses. I have collected over 700 tools that can help researchers do their work efficiently. It is assembled as an interactive Web-based mind map, titled Research Tools, which is updated periodically. Created using the MindMeister software platform, Research Tools provides a convenient interface with zoom in/out, screen drag, and other user- friendly features.

Being a mind map, Research Tools consists of a hierarchical set of nodes. It has four main nodes: (1) Searching the literature, (2) Writing a paper, (3) Targeting suitable journals, and (4) Enhancing visibility and impact, and six auxiliary nodes. In the mind map, each parent node has several child nodes associated with it, which can be accessed by clicking on the plus (+) sign. Several free tools can be found in the child nodes. Some paid tools are also included.

The four main nodes are described below, listing some tools as examples. In the mind map, a green tick sign indicates my preference for some tools. Users can access the tools by clicking on the arrow sign that appears next to the tool title.

1. Searching the Literature

Tools under this node would assist first-year research students and other early-stage researchers in expanding their knowledge and developing a research focus (Ale Ebrahim, 2013). A sample of these tools is given below, grouped under the specific purposes for which these tools might be used:

(a) Find keywords: Google AdWords, Google Trends, Springer Realtime, KwMap (b) Develop a new keyword/phrase: Microsoft Adcenter Lab, keyword density analyser, Free Keyword Tool (c) Find research materials: Web of Science, Scopus®, ProQuest, Research Papers in Economics, Munich Personal RePEc Archive (MPRA) (d) Evaluate a paper: Publish or Perish, Readermete, Microsoft Academic Research (e) Access databases: Network, Refdoc, iSEEK Education, Database of Electrical Engineering & Computer Science (f) Compare resources: Top 30 Web Tools to Search, 100 Useful Tips and Tools to Research the Deep Web (g) Search visually: Kartoo, Redz

2. Writing a Paper

Several tools on research writing have been assembled under this node. These tools help researchers in various aspects of scientific and scholarly writing. Here is a sample of such tools:

(a) Write and edit: Ginger, Spell Checker, Plagiarisma, Springer Exemplar (b) Analyse and summarise texts: Smmry, Tools 4 Noobs, Free Summarizer (c) Search desktop: DtSearch, Copernic Desktop Search (d) Write collaboratively: Google Drive, Adobe acrobat professional (e) Synthesise and visualise information: MindMeister, MindMaple, Text2mindmap (f) Manage bibliographic data: Mendeley, EndNote, Zotero (g) Build and share article collections: Bibliogo, WizFolio (h) Detect and avoid plagiarism: Viper, The Plagiarism Checker, Turnitin, Paper Rater

3. Targeting Suitable Journals

Sometimes, researchers are not sure about the journal they should target for publishing their research papers. They would like to evaluate a journal’s visibility and credibility before submitting their papers. Researchers can use a range of tools to identify, assess, and select appropriate journals with a view to enhancing their publication profile (Ale Ebrahim, 2010).

(a) Identify potential journals: Elsevier journal finder, JANE (from The Biosemantics Group), Springer journal advisor (b) Detect relevant journals: Google Alerts, My Citation Alerts (from Web of Knowledge) (c) Evaluate journal quality (impact factor and journal ranking): Journal Citation Report (Thomson Reuters), SJR (Scimago Lab), Eigenfactor® (University of Washington)

4. Enhancing Visibility and Impact

Publishing a research paper in a scholarly journals is necessary but not sufficient for receiving citations in future. We need to ensure that the paper is visible to the relevant users and authors. There are tools that help in enhancing the visibility and readership of research papers. Effective use of these tools can result in increased citations and, thus, improve the h-index of the author (h-index shows the author’s influence as a researcher, see Ale Ebrahim, 2012; Aghaei Chadegani et al., 2013). Here is a sample of tools to increase the visibility of one’s published papers:

(a) Create an online CV: ResearcherID, Publications List, ORCID, Google Scholar My Citations, A visual resume (b) Create an online repository: CiteuLike, Getcited, PubZone, Social Science Open Access Repository, arXiv, HAL (c) Share bookmarks: Diigo, Delicious, BibSonomy, ScienceWISE (d) Publish and share information: Scribd, Docstoc®, figshare, Udemy (e) Network with other researchers: ResearchGate, Academia, LinkedIn

5. Auxiliary Nodes

Research tools which could not be categorised under the above four main nodes have been placed under six auxiliary nodes. The auxiliary nodes are: Survey, Links, Keeping up-to-date (Alert service), h-index, General, and Download. The tools under these nodes can assist researchers in keeping track of their research interest and activities in various ways.

(a) Stay current with research: Science direct alerts, MIT press, PubMed, Conference Alerts, WikiCFP (b) Collect data: SurveyMonkey, Formsite, Survey share, Google Docs, eSurveysPro, Free Online Survey, KwikSurvey (c) h-index: Prediction scientific h-index, Finding h-index in Web of Science, Optimizing your article for search engines, 10 Ways to Increase Usage and Citation of your Article Using Social Media

6. Conclusion

We can save plenty of research time by using specific tools for specific purposes. This can facilitate quick progress and early completion of research projects. In my experience, research students who use these tools are able to complete their literature review in less than 3 months.

Acknowledgement

Sincere thanks to Professor D. P. Dash for his kind encouragement to write this article and his useful advice on its organisation.

References

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. Retrieved from http://dx.doi.org/10.5539/ass.v9n5p18

Ale Ebrahim, N. (2010). Target ISI journals: How to write/publish ISI papers [Presentation material]. Retrieved from http://works.bepress.com/aleebrahim/1/

Ale Ebrahim, N. (2012). Publication marketing tools: Enhancing research visibility and improving citations [Presentation material]. Retrieved from http://works.bepress.com/aleebrahim/64/

Ale Ebrahim, N. (2013). The effective use of research tools and resources. [Presentation material]. Retrieved from http://works.bepress.com/aleebrahim/73/

Suggested Citation: Ale Ebrahim, N. (2013). Introduction to the Research Tools mind map. Research World, 10, Article A10.4. Retrieved from http://www1.ximb.ac.in/RW.nsf/pages/A10.4

Copyleft The article may be used freely, for a noncommercial purpose, as long as the original source is properly acknowledged.

Xavier Institute of Management, Xavier Square, Bhubaneswar 751013, India Research World (ISSN 0974-2379) http://www1.ximb.ac.in/RW.nsf/pages/Home

American J. of Engineering and Applied Sciences 5 (1): 9-14, 2012 ISSN 1941-7020 © 2012 Science Publications

Technology Use in the Virtual R&D Teams

1Nader Ale Ebrahim, 2Shamsuddin Ahmed, 1Salwa Hanim Abdul Rashid and 2Zahari Taha 1Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya Kuala Lumpur, Malaysia 2Faculty of Manufacturing Engineering and Management Technology, University Malaysia Pahang, 26300 Gambang, Pahang, Malaysia

Abstract: Problem statement: Although, literature proves the importance of the technology role in the effectiveness of virtual Research and Development (R&D) teams for new product development. However, the factors that make technology construct in a virtual R&D team are still ambiguous. The manager of virtual R&D teams for new product development does not know which type of technology should be used. Approach: To address the gap and answer the question, the study presents a set of factors that make a technology construct. The proposed construct modified by finding of the field survey (N = 240). We empirically examine the relationship between construct and its factors by employing the Structural Equation Modeling (SEM). A measurement model built base on the 19 preliminary factors that extracted from literature review. The result shows 10 factors out of 19 factors maintaining to make technology construct. Results: These 10 technology factors can be grouped into two constructs namely Web base communication and Web base data sharing. The findings can help new product development managers of enterprises to concentrate in the main factors for leading an effective virtual R&D team. In addition, it provides a guideline for software developers as well. Conclusion: The second and third generation technologies are now more suitable for developing new products through virtual R&D teams.

Key words: Collaboration teams, questionnaires performance, cross-functional teams, product development, structural equation modeling, measurement model, literature review

INTRODUCTION even around the world. Therefore, enterprises have no choice but to disperse their new product units to access Virtual teams are defined as “small temporary such dispersed knowledge and skills (Kratzer et al ., groups of geographically, organizationally and/or time 2005). As a result, enterprises are finding that internal dispersed knowledge workers who coordinate their development of all technology needed for new products work, mainly with electronic information and and processes are difficult or impossible. They must communication technologies to carry out one or more increasingly receive technology from external sources organization tasks” (Ebrahim et al ., 2009b). Virtual (Stock and Tatikonda, 2004). R&D team is a form of a virtual team, which includes Virtualization in NPD has recently started to make the features of virtual teams and concentrates on R&D serious headway due to developments in technology- activities (Ebrahim et al ., 2011). The members of a virtuality in NPD which is now technically possible virtual R&D team use different degrees of (Leenders et al ., 2003). As product development communication technology to complete the research becomes the more complex, supply chain, also have to without space, time and organizational boundaries collaborate more closely than in the past. These kinds (Nader et al ., 2010a, Husain and Yong, 2009). “We are of collaborations almost always involve individuals becoming more virtual all the time!” is heard in many from different locations, so virtual team working global corporations today (Chudoba et al ., 2005). On supported by Information Technology (IT), offers the other hand, New Product Development (NPD) is notable potential benefits (Anderson et al ., 2007). widely recognized as a key to corporate prosperity Although the use of the internet in NPD has received (Lam et al ., 2007). The specialized skills and talents notable attention in the literature, little is written about needed for developing new products often remain collaborative tool and effective virtual teams for NPD locally in pockets of excellence around the company or (Ebrahim et al ., 2009a). In addition, literature shows the Corresponding Author: Nader Ale Ebrahim, Department of Engineering Design and Manufacture, Faculty of Engineering, University of Malaya Kuala Lumpur, Malaysia 9 Am. J. Engg. & Applied Sci., 5 (1): 9-14, 2012

factors that make technology construct in a virtual R&D Factors that make technology construct in a virtual team are still ambiguous. I this study we try to fill the R&D team are still ambiguous. We extracted 19- gap in the literature. importance factors related to the technology construct, This study is structured as follows. First, base on base on a comprehensive review on technology view in prior research we extract the 19 factors of technology the virtual R&D team working. Table 1 summarized the construct in the virtual R&D teams. Next, Structural factors and their supported references. E-mails and Equation Modeling (SEM) is used as the analytical tool conference calls are generally known as first generation for testing the estimating and testing the technology technologies while online discussion boards, power construct measurement models. Then adjust the point presentations, video tools and online meeting preliminary technology construct the model by fitting tools are second-generation technologies. Third the model according to the SEM fitness indices and generation technology refers typically to web-enabled made a final measurement model. The study infers with shared workspaces with the intranet or internet (Lee- a discussion and future guidelines. Kelley and Sankey, 2008).

Literature review: Virtual teams use digital Research method: To build a measurement model of communications, video and audio links, electronic whiteboards, e-mail, instant messaging, websites, chat technology construct in virtual R&D teams for new product development, we conducted a web-based rooms, as substitutes for physical collocation of the team members (Baskerville and Nandhakumar, 2007, survey mainly in Malaysian and Iranian manufacturing enterprises, in a random sample of small and medium Pauleen and Yoong, 2001). Simple transmission of information from point A to point B is not enough; the enterprises. Web-based survey method is selected because; it is a cost-effective and quick result to get virtual environment presents significant challenges to effective communication (Walvoord et al ., 2008). Being feedback from the belief of the respondent. A Likert scale from one to five was used. This set up gave equipped with even the most advanced technologies are not enough to make a virtual team effective, since the respondents a series of attitude dimensions. For each factor, the respondent was asked whether, the factor is internal group dynamics and external support mechanisms must also be present for a team to succeed in not important or extremely important by using a Likert scale rating. The questionnaire was emailed to the the virtual world (Lurey and Raisinghani, 2001). Virtual teams are technology-mediated groups of people from managing director, R&D manager, the new product development manager, project and design manager and different discipline that work on common tasks (Dekker et al ., 2008) so the way the technology is implemented seems suitable people who were most familiar with the R&D activities in the firm. The rapid expansion of Internet to make the virtual teams outcome more or less likely (Anderson et al ., 2007). Virtual R&D team instructor users has given web-based surveys the potential to become a powerful tool in survey research (Sills and should choose the suitable technology based on the Song, 2002, Nader et al ., 2010b). purpose of the team (Ebrahim et al ., 2009c).

Table 1: Summary of the factors related to the technology construct in the virtual teams Factor name Factor References Tech1 Use internet and electronic mail (Redoli et al ., 2008, Pauleen and Yoong, 2001, Lee-Kelley and Sankey, 2008, Thissen et al ., 2007) Tech2 Online meeting on need basis (Chen et al ., 2007; Lee-Kelley and Sankey, 2008; Pena-Mora et al ., 2000; Thissen et al ., 2007) Tech3 Web conferencing (Coleman and Levine, 2007; Thissen et al ., 2007, Zemliansky and Amant, 2008; Ebrahim et al ., 2009c) Tech4 Seminar on the Web (Zemliansky and Amant, 2008) Tech5 Shared work spaces (Lee-Kelley and Sankey, 2008) Tech6 Video conferencing (Chen et al ., 2007; Zemliansky and Amant, 2008) Tech7 Audio conferencing (Chen et al ., 2007; Lee-Kelley and Sankey, 2008; Zemliansky Tech8 Online presentations (Lee-Kelley and Sankey, 2008) and Amant, 2008) Tech9 Share documents (off-line) (Coleman and Levine, 2007; Ebrahim et al ., 2009c) Tech10 Share what’s on your computer desktop with people in other locations (Remote access and control) (Thissen et al ., 2007; Ale et al ., 2009) Tech11 Do not install engineering software (Coleman and Levine, 2007; Kotelnikov, 2007, (get service through web browser) Vasileva, 2009) Tech12 Access service from any computer (in Network) (Thissen et al ., 2007; Vasileva, 2009) Tech13 Standard phone service and hybrid services (Thissen et al ., 2007; Ebrahim et al ., 2009c) Tech14 Access shared files anytime, from any computer (Lee-Kelley and Sankey, 2008) Tech15 Web database (Coleman and Levine, 2007; Zemliansky and Amant, 2008; Ebrahim et al ., 2009c) Tech16 Provide instant collaboration (Coleman and Levine, 2007; Thissen et al ., 2007) Tech17 Software as a service (canceling the need to install and run the application on the own computer) (Coleman and Levine, 2007; Thissen et al ., 2007) Tech18 Virtual research center for product development (Zemliansky and Amant, 2008) Tech19 Can be integrated/compatible with the other tools and systems (Coleman and Levine, 2007; Kotelnikov, 2007) 10 Am. J. Engg. & Applied Sci., 5 (1): 9-14, 2012

Invitation e-mails were sent to each respondent, Convergent validity was established using a reaching 972 valid email accounts, with reminders calculation of the factor loading, Average Variance following every two weeks up to three months. 240 Extracted (AVE) and Composite Reliability (CR). The enterprises completed the questionnaire, for an overall factors that have standardized loadings exceeded 0.50, response rate of 24.7% Table 2. were maintained (Dibrell et al ., 2008). The initial measurement model was consisting of 19 factors (Tech1 RESULTS to Tech19). After revising the measurement model by Anderson and Gerbing (1988) suggested using deleting Tech1, Tech10, Tech11 and Tech13, the AVE Confirmatory Factor Analysis (CFA) for scale and CR were calculated. AVE larger than 0.5 is the development because it affords stricter interpretation of threshold (McNamara et al ., 2008). CR is calculated by unidimensionality than what is provided by more squaring the sum of loadings, then dividing it by the sum traditional approaches, such as coefficient alpha, item- of squared loadings, plus the sum of the measurement total correlations and exploratory factor analysis. The error (Lin et al ., 2008). CR should be greater than 0.6 evidence that the measures were one-dimensional, (Huang, 2009). The measurement model had acceptable where a set of indicators (factors) shares only a single convergent validity since the calculated CR and AVE underlying construct, was assessed using CFA were 0.930 and 0.613 respectively. (Anderson and Gerbing, 1988). After data collection, For discriminant validity, we performed AMOS the measures purification procedures should be used to software using Maximum Likelihood method (ML). assess their reliability, unidimensionality, discriminate The fitting indices checked with their respective validity and convergent validity (Anderson and acceptance values Table 4. We run the AMOS for the Gerbing, 1988). model Ver1 (technology construct with 15 factors) and For reliability analysis, Cronbach’s Alpha found a nonsignificant chi-square per degrees of (Cronbach, 1951) was employed to each factor. As freedom (CMIN/DF = 7.232). Most of the rest of fit shown in Table 3, all the items with Cronbach’s α indices was not in the acceptable range. greater than threshold 0.6 were included in the analysis and the rest omitted from analysis. So, the factors Table 3: Summary of the final measures and reliabilities Tech1, Tech10, Tech11 and Tech13 freed from further Corrected item- Cronbach’s alpha Factor name total correlation if Item deleted analysis. In general, the reliability of the questionnaire’s Tech1 0.525 0.943 instruments displayed a good reliability across samples. Tech2 0.755 0.939 Structural Equation Modeling (SEM) using AMOS Tech3 0.777 0.939 18 was employed for validation of the measurement Tech4 0.717 0.940 model. This statistical analysis are estimated Tech5 0.759 0.939 Tech6 0.722 0.940 simultaneously for both the measurement and structural Tech7 0.731 0.939 models (Dibrell et al ., 2008). To ensure the factors Tech8 0.780 0.939 make a right construct, the measurement model Tech9 0.610 0.942 examined for model fit. Given this, the model assessed Tech10 0.576 0.942 Tech11 0.571 0.943 for the convergent and discriminant validity. Tech12 0.686 0.940 Tech13 0.519 0.943 Table 2: Summarized online survey data collection Tech14 0.624 0.941 Numbers of emails sent enterprises 3625 Tech15 0.696 0.940 Total responses (Click the online web page) 972.0 Tech16 0.642 0.941 Total responses / received questionnaire (%) 26.8 Tech17 0.678 0.940 Total completed 240.0 Tech18 0.649 0.941 Total completed / received questionnaire (%) 24.7 Tech19 0.615 0.942

Table 4: Fitting indices (adopted from (Byrne, 2001) Fit Indices Desired range χ2 /degrees of freedom (CMIN/DF) ≤ 2.00 IFI (Incremental Fit Index) ≥ 0.90 Comparative Fit Index (CFI) Coefficient values range from zero to 1.00, with values close to 0.95 showing superior fit RMSEA (Root Mean Squire values less than .05 show good fit and values as high as .08 represent reasonable fit, Error of Approximation) from 0.08-0.10 show mediocre fit and those greater than 0.10 show poor fit Root mean square residual (RMR) ≤ 0.08 Goodness-of-Fit Index (GFI) ≥ 0.90 Normed Fit Index (NFI) Coefficient values range from zero to 1.00, with values close to 0.95 showing superior fit Relative Fit Index (RFI) Coefficient values range from zero to 1.00, with values close to 0.95 showing superior fit Tucker-Lewis Index (TLI) Values ranging from zero to 1.00, with values close to 0.95 (for large samples) being indicative of good fit 11 Am. J. Engg. & Applied Sci., 5 (1): 9-14, 2012

Fig. 2: Final measurement model

Fig. 1: Measurement model Ver2 The results of the final measurement model of Thus, refer to the AMOS Modification Indices (MI) technology construct in virtual R&D team for some of the factors that had the lowest factor loading or developing a new product, shows the share of two main the same effect of remaining factor, were deleted. With contrasts, which are strongly correlated to each other: this modification, the measurement model Ver2 had a significant chi-square per degrees of freedom • Web base communications consists of online (CMIN/DF = 4.767); other fit indices, RMSEA, RMR meeting on needed basis, web conferencing, and GFI also were in the acceptable range. Therefore, seminar on the web, video conferencing, audio the best fitting model was the measurement model Ver2 conferencing and online presentations • Fig. 1 and it used for further analysis. Web base data sharing consists of shared work spaces, share documents (off-line), access service DISCUSSION from any computer (in network) and virtual research center for product development The final measurement developed made base on the measurement model ver2 by classifying the factors in According to Lee-Kelley and Sankey (2008) these two groups according to their relevant factor loading two constructs belong to the second and third with the threshold 0.83. The proper name for each generation of technology. Equip virtual R&D team group can be web base; communications and data members with the suitable technology make the teams sharing respectively. As displayed in Fig. 2 each factor more effective. Therefore, the manager of NPD should loading was above 0.62 and significant. Overall, the provide the facilities and infrastructures for the virtual final measurement model produced good fit indices R&D teams to achieve the higher level of team effectiveness. (CMIN/DF = 2.889, RMR = .04, GFI = 0.929, RFI =

0.929, NFI = 0.949, TLI = 0.952, CFI = 0.966 IFI = CONCLUSION 0.964, RMSEA = 0.089).

While fitting the technology construct the Research so far has explored the 19 factors for measurement model the factors Tech14 (access shared working together virtually; however, us still less know files anytime, from any computer), Tech15 (web about the factors which has main contributions in the database), Tech16 (provide instant collaboration), technology construct of the virtual R&D teams for New Tech17 (software as a service (eliminating the need to product development. The findings of this study extend install and run the application on the own computer)) the literatures and help to build a foundation for further and Tech19 (can be integrated/compatible with the understanding of the technology elements in the virtual other tools and systems) were dropped. Modification R&D teams for new product development. The indices (MI) base on regression weights shows Tech17, measurement model shows ten factors that make the Tech 18 and Tech19 are highly correlated, so one technology constructs. These ten factors can be sorted representative (Tech18) from this group is enough. by their factor loading which are reflecting the factor Tech14 to Tech16 are strongly correlated with Tech12, weight. Therefore, the software developer or the so the remaining factor represents the deleted ones. managers of the NPD are able to provide a better 12 Am. J. Engg. & Applied Sci., 5 (1): 9-14, 2012 platform for virtual team working by concentrating on Dekker, D.M., C.G. Rutte and V.D.P.T. Berg, 2008. the main factors. The second and third generation of Cultural differences in the of critical technology (refer to definition of Lee-Kelley and interaction behaviors in global virtual teams. Int. J. Sankey (2008) is now more suitable for developing a Intercu. Relations, 32: 441-452. 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