Post on 23-Aug-2014
description
The relation between the number of countries-Rich Files on the web and countries-economic
development
Hamzehali Nourmohammad1
Abdalsamad Keramatfar2
Economic Development, GDP, Rich Files, Science Indicators, Webometrics
Abstract
Price demonstrated the correlation between countries‟ scientific productivity and their GDP and presented
the relation between scientific dynamism and economic development to show the important of scientific
researches and to verify the paper counting approach and, Nourmohammadi and Keramatfar
demonstrated that there exists a correlation between countries‟ scientific production rank and their Rich
Files rank on the web and concluded that scientific evaluation of countries could be done based on the
number of their Rich Files on the web. In this paper we examine the correlation between countries‟ Rich
Files and their GDP. The results show that, there is a higher correlation between them than previous
correlation. The high degree of correlation between rank base on Rich Files and rank base on economic
development signifies the significance of web as the context of research and free access to information
resources so policy makers in every country should be informed of it.
Introduction
All the activities related to measuring science started in early 20th century with the works of people like
Holm(Braun & others,1985), following Price‟s attempts to display the relation between scientific products
and countries‟ scientific development, using citation indexes for examining countries‟ scientific
development expanded rapidly. In addition, late in 1960s, Price demonstrated the correlation between
countries‟ scientific productivity and their GDP and presented the relation between scientific dynamism
and economic development (Noroozi Chakoli, 2012). Within the past years this correlation has been
1 Shahed University, Persian Gulf Highway, Tehran, Iran
2 Scientific Information Database SID, Tehran, Iran
confirmed by many researchers like, Vinkler (2008) and Lee & others (2011), which both indicates the
significance of evaluation of researches‟ findings and verifies its method which is using excessive c itation
indexes. On the other hand, since the mid-1990s has emerged a new research field, webometrics-
“webometrics” itself was coined in 1997 (Almind and Ingwersen, 1997), investigating the nature and
properties of the Web drawing on modern informetric methodologies (Björneborn & Ingwersen, 2001).
The value of webometrics quickly became established through the Web Impact Factor, the key metric for
measuring and analyzing website hyperlinks (Thelwall, 2012). Also the need for timely and relevant web-
based S&T indicators has become more urgent (Scharnhorst & Wouters, 2006). Nourmohammadi and
Keramatfar (2013) demonstrated that there exists a correlation between countries scientific production
rank and their Rich Files rank on the web and concluded that scientific evaluation of countries could be
done based on the number of their Rich Files on the web. According to what was mentioned above, the
main problem this study seeks to address is this; is there any relation between countries‟ Rich Files on
the web and their economic development?
Therefore, the questions this study addresses are as follows:
- What is the number of scientific production of world‟s different countries?
- What is the number of different countries‟ Rich Files on the web?
- What is the amount of GDP indicator of world‟s different countries?
- What is the amount of correlation between countries‟ scientific production rank and their GDP rank in
comparison with the correlation between countries‟ Rich Files rank and their GDP rank?
- How is the linear relation between the number of countries‟ Rich Files and their GDP?
Methodology
This study is descriptive -based and due to its use of Scientometrics methods lies within scope of
Webometrics Researches. Countries‟ scientific production data was extracted from SCImago and
countries‟ GDP data was extracted from World Bank. Countries‟ Rich Files data was extracted from Bing
search engine in the following way; in order to search, the name of a given country was chosen in the
Advance Search section then using the formulae: filetype:pdf, filetype:doc, filetype:ppt, the number of
Rich Files was determined. Correlation Test was carried out using SPSS19, and Regression Test was
carried out using Excel 2007. Research Society included all the world countries for which there is the
possibility of specific search in Bing search engine. Data was extracted in the second half of August 2013.
Theoretical framework
Nowadays scientific production is measured based on excessive citation indexes that present
bibliographical information of different kinds of scientific productions, because citation index makes
identifying and recovering valid information about subject areas possible and provides citation information
that relates papers and indicates the degree of validity of papers to a great extent (Noroozi Chakoli,
2013). Using the number of countries‟ scientific productions in order to evaluate their scientific
development by experts is done by the two large databases ESA and SCImago, the former using Web of
Knowledge data and the latter using Scopus data.
Along with developments in bibliometrics and emergence of Webometrics some attempts were made to
use the web for scientific evaluation. Webometrics is the quantitative analysis of web phenomenon using
informetric methods (Noroozi Chakoli, 2012). A useful database in this field is Webometrics3 that has
been evaluating universities across the world according to their website since2007. One of the indicators
of this database is the number of universities‟ Rich Files on the web. Rich Files include PDF, DOC, and
PPT; these files have been chosen because the majority of scientific productions are published in one of
these formats. Nourmohammadi & Keramatfar (2013) by demonstrating the correlation between the
number of countries‟ Rich Files on the web and the number of their scientific production proposed that
Rich Files can be used for evaluating countries‟ scientific development. In this study, the authors examine
Nourmohammadi & Keramatfar‟s proposal and by examining its correlation with countries‟ economic
development compare this method with excessive citation indexes method.
Findings:
3 Webometrics.info
The findings will be presented in four sections according to the questions put forward in the introduction.
1. What is the number of scientific production of world’s different countries?
Table No1 shows the number of world countries‟ scientific productions in SCImago. USA, UK, and
Japan are ranked first, second, and third.
Table No1. The number of countries’ document in SCImago
Country Document
s
Country Documen
ts
Country Documents
United
States
6,149,455 Portugal 117,469 Philippines 11,326
United
Kingdom
1,711,878 New
Zealand
114,495 Puerto Rico 9,862
Japan 1,604,017 South
Africa
107,976 Iceland 9,285
Germany 1,581,429 Argentina 105,216 Latvia 8,396
France 1,141,005 Hungary 100,137 Armenia 8,054
Canada 885,197 Ukraine 98,083 Peru 7,516
Italy 851,692 Ireland 91,125 Oman 6,875
Spain 665,977 Romania 76,361 Georgia 6,381
India 634,472 Egypt 75,610 Azerbaijan 6,135
Australia 592,533 Malaysia 75,530 Costa Rica 5,711
Russian
Federation
527,442 Thailand 69,637 Luxembour
g
5,121
South Korea 497,681 Chile 58,768 Iraq 4,420
Netherlands 487,784 Slovakia 49,863 Macedonia 4,401
Brazil 391,589 Croatia 49,462 Qatar 4,398
Taiwan 351,610 Pakistan 47,443 Ecuador 3,887
Switzerland 350,253 Saudi
Arabia
46,167 Bosnia and
Herzegovin
a
3,524
Sweden 337,135 Slovenia 44,142 Syrian Arab
Republic
3,379
Poland 304,003 Tunisia 32,250 Panama 3,043
Turkey 267,902 Colombia 28,817 Bahrain 2,817
Belgium 265,913 Morocco 23,446 Libyan
Arab
Jamahiriya
2,304
Israel 204,262 Lithuania 21,098 Bolivia 2,298
Austria 188,440 Algeria 21,059 Malta 2,029
Denmark 183,880 Serbia 21,011 Yemen 1,395
Finland 170,476 Jordan 17,126 Guatemala 1,296
Greece 160,760 Estonia 16,573 Albania 1,229
Iran 159,046 Indonesia 16,139 Nicaragua 818
Mexico 144,997 United
Arab
Emirates
15,698 Paraguay 776
Hong Kong 144,935 Kenya 14,765 El Salvador 768
Czech
Republic
142,090 Viet Nam 13,172 Dominican
Republic
606
Norway 141,143 Kuwait 12,254 Honduras 595
Singapore 126,881 Lebanon 11,672
2. What is the number of different countries’ Rich Files on the web?
Table No2 shows the number of Rich Files for different world countries, with USA, Japan, and Italy
having the highest number of Rich Files on the web respectively.
Table No2. The number of countries‟ Rich Files on the web
Country PDF DOC PPT SUM
Albania 16100 6720 71 22891
Algeria 46200 5130 1220 52550
Argentina 1190000 158000 25400 1373400
Armenia 13300 3190 1530 18020
Australia 2960000 171000 18800 3149800
Austria 1090000 42800 8560 1141360
Azerbaijan 12000 4490 61 16551
Bahrain 7820 101 44 7965
Belgium 1280000 98900 16600 1395500
Bolivia 64200 7610 1350 73160
Bosnia and
Herzegovina
68300 11000 1480 80780
Brazil 4800000 399000 100000 5299000
Canada 4370000 202000 67900 4639900
Chile 639000 67400 22500 728900
Colombia 872000 93800 14100 979900
Costa Rica 127000 24600 14100 165700
Croatia 377000 54200 13700 444900
Czech Republic 708000 101000 22700 831700
Denmark 1070000 74200 11500 1155700
Dominican Republic 42900 2490 734 46124
Ecuador 169000 17200 3970 190170
Egypt 49400 11400 4550 65350
El Salvador 51700 2810 1040 55550
Estonia 161000 23500 8010 192510
Finland 883000 46300 11900 941200
France 5930000 351000 88300 6369300
Georgia 25500 4530 641 30671
Germany 8320000 264000 121000 8705000
Greece 553000 89900 11500 654400
Guatemala 69400 3610 1090 74100
Honduras 24800 1000 90 25890
Hong Kong S.A.R. 704000 60000 21000 785000
Hungary 672000 137000 24500 833500
Iceland 54200 4270 2230 60700
India 1500000 105000 2230 1607230
Indonesia 669000 83400 27600 780000
Iran 536000 93400 19800 649200
Iraq 14800 5460 66 20326
Ireland 528000 47700 8940 584640
Israel 387000 215000 35800 637800
Italy 9660000 978000 120000 10758000
Japan 13100000 444000 39400 13583400
Jordan 24700 9700 4050 38450
Kenya 27700 2490 792 30982
Kuwait 13100 1890 63 15053
Latvia 114000 51700 3510 169210
Lebanon 23300 3070 1150 27520
Libya 5230 81 31 5342
Lithuania 210000 63600 7910 281510
Luxembourg 78800 3640 766 83206
Macedonia 33100 4660 600 38360
Malaysia 378000 28600 6720 413320
Malta 24800 1690 1860 28350
Mexico 2770000 308000 40400 3118400
Morocco 64600 6720 1570 72890
Netherlands 3570000 252000 36800 3858800
New Zealand 592000 48100 8730 648830
Nicaragua 24800 2060 911 27771
Norway 682000 57600 15000 754600
Oman 7630 2390 47 10067
Pakistan 101000 11400 2100 114500
Panama 60100 4380 1230 65710
Paraguay 23900 2650 1570 28120
Peru 633000 80500 11700 725200
Philippines 77200 4990 1510 83700
Poland 3400000 715000 45800 4160800
Portugal 934000 33900 10200 978100
Puerto Rico 84000 8580 4970 97550
Qatar 12500 1490 61 14051
Romania 737000 152000 18500 907500
Russia 2140000 2150000 147000 4437000
Saudi Arabia 88400 38400 20800 147600
Serbia 187000 20000 6300 213300
Singapore 352000 19000 3960 374960
Slovakia 397000 63800 10400 471200
Slovenia 284000 45200 20800 350000
South Africa 852000 71700 11800 935500
South Korea 686000 30900 67700 784600
Spain 6310000 334000 80500 6724500
Sweden 2540000 148000 21200 2709200
Switzerland 2420000 88300 22100 2530400
Syria 9330 980 45 10355
Taiwan 1320000 603000 127000 2050000
Thailand 1220000 310000 57000 1587000
Tunisia 36100 2350 891 39341
Turkey 1030000 229000 44900 1303900
United Arab Emirates 47700 4830 1520 54050
Ukraine 243000 128000 7490 378490
United Kingdom 6730000 626000 108000 7464000
United States 47500000 3870000 1380000 52750000
Vietnam 141000 135000 4030 280030
Yemen 710 43 3 756
3. What is the amount of GDP indicator of world’s different countries?
Table No3 shows countries‟ GDP with USA, Japan, and Germany having the highest GDP
respectively.
Table No.3. Countries‟ GDP
Country GDP Country GDP
Albania 13119013351.4499 Lebanon 42945273631.8408
Algeria 207955103846.43 Libya -
Argentina 474865096195.534 Lithuania 42245532390.1713
Armenia 9910387657.35811 Luxembourg 57117125224.9936
Australia 1520608083022.1 Macedonia 9663203711.45536
Austria 399649131196.966 Malaysia 303526203366.211
Azerbaijan 67197738734.7695 Malta 8721923076.92308
Bahrain - Mexico 1177271329643.86
Belgium 483709179737.722 Morocco 96729450169.498
Bolivia 27035110167.0902 Netherlands 772226793520.185
Bosnia and
Herzegovina
17047582419.997 New Zealand -
Brazil 2252664120777.39 Nicaragua 10507356837.651
Canada 1821424139311.45 Norway 499667211001.289
Chile 268313656098.796 Oman -
Colombia 369812739540.023 Pakistan 231181921489.54
Costa Rica 45127292711.0687 Panama 36252500000
Croatia 56441607483.0696 Paraguay 25502060502.1181
Czech Republic 195656544502.618 Peru 197110985681.958
Denmark 314242037116.962 Philippines 250265341493.171
Dominican
Republic
58951239185.7506 Poland 489795486644.151
Ecuador 84532444000 Portugal 212454101311.391
Egypt 257285845358.245 Puerto Rico 101495811266
El Salvador 23786800000 Qatar -
Estonia 21854197100.7971 Romania 169395940257.194
Finland 250024427873.489 Russia 2014774938341.85
France 2612878387760.35 Saudi Arabia -
Georgia 15829300978.6172 Serbia 37488935009.7878
Germany 3399588583183.34 Singapore 274701299733.694
Greece 249098684277.449 Slovakia 91619230769.2308
Guatemala 50806430481.5925 Slovenia 45469230769.5781
Honduras 17967497441.1464 South Africa 384312674445.534
Hong Kong S.A.R. 263259372904.956 South Korea 1129598273324.48
Hungary 125507525410.477 Spain 1349350732836.2
Iceland 13656532879.6765 Sweden 525742140221.402
India 1841717371769.71 Switzerland 632193558707.476
Indonesia 878043028442.369 Syria -
Iran - Taiwan -
Iraq 210279947255.575 Thailand 365564375701.58
Ireland 210330986079.969 Tunisia 45662043358.0705
Israel - Turkey 789257487307.029
Italy 2013263114238.88 Ukraine 176308825694.203
Japan 5959718262199.13 United Arab
Emirates
-
Jordan 31243324000 United
Kingdom
2435173775671.41
Kenya 37229405066.6773 United States 15684800000000
Kuwait - Vietnam 141669099289.418
Latvia 28373857404.0219 Yemen 35645823131.5726
4. What is the amount of correlation between countries’ scientific production rank and their
GDP rank in comparison with the correlation between countries’ Rich Files rank and their
GDP rank?
Tables No.4 and No.5 show the correlation between GDP and the two indicators of countries scientific
production rank and countries Rich Files rank.
Table No.4. correlation between countries‟ scientific
production rank and their GDP rank in comparison
GDP
DOC Correlation
Coefficient
.879**
Sig. (2-tailed) .000
N 80
Correlation is significant at the 0.01 level (2-tailed)
Table No.5. correlation between countries‟ Rich Files rank
and their GDP rank.
GDP
RICH Correlation
Coefficient
.897**
Sig. (2-tailed) .000
N 80
Correlation is significant at the 0.01 level (2-tailed)E
Conclusion and Discussion
Nowadays web and web databases are the first and the most important source for researchers to find
information and web richness of every country as its scientific backbone is of highest importance.
Moreover, free access to information resources is the context for expanding researches. Existence of
scientific resources could be used as a criterion for scientific evaluation (Nourmohammadi & Keramatfar,
2013). The present study sought to investigate the correlation between countries‟ Rich Files rank and
their economic development rank. The findings indicate that there is a high degree of correlation between
the rankings of these two variables. Compared with the correlation between countries‟ scientific
development Ranking and countries‟ economic development ranking (that also has been showed by
(King, 2004) and (Price, 1978) and (Kealey, 1996)), this correlation does have a higher amount that
means this variable has a greater correlation with economic development than science production
indicator. The high degree of correlation between this variable and economic development signifies the
significance of web as the context of research and free access to information resources. Moreover this
correlation demonstrates that this variable can be used along with other indicators to evaluate countries‟
scientific development. Another point worth noticing is the fact that having access to web, disregarding
the initial expenses, is free and evaluation according to this can be easily done, while having access to
databases like Web of Knowledge and Scopus involves expenditure; however, it should be taken into
account that due to the dynamic nature of web and its constant and rapid changes, Webometric results
have always been tentative. Other researches following this study can be concerned with the evaluation
of the nature of these files and their types –article, manual, handbook, book, etc.; meanwhile conducting
causality test between these two variables can result in helpful findings.
Sources
Almind T. C., & Ingwersen P. 1997. Informetric analyses on the World Wide Web: methodological
approaches to „webometrics‟. Journal of documentation, 53(4): 404-426.
Björneborn L, & Ingwersen P, 2001. Perspective of webometrics. Scientometrics, 50(1): 65-82.
Braun T., Glänzel W., & Schubert, A, 1985. Scientometric Indicators: A Thirty-Two Country Comparative
Evaluation of Publishing Performance and Citation Impact. World Scientific.
Kealey T, Nelson R. 1996. The economic laws of scientific research. Macmillan, London.
Lee L, Pin-hua, L, Chung Y, Lee, Y, 2011. Research output and economic output: a Granger causality
test. Scientometrics, 89(2): 465-478.
Noroozi Chakoli A, 2012. Introduction to Scientometrics. Samt, Tehran.
Nourmohammadi H, Keramatfar A. 2013. Assessment of scientific presence of Estonia in web; a new
Approach. In: Proceeding of WIS 2013, Estonia, 9th International Conference on Webometrics,
Informetrics and Scienctometrics & 14th COLLNET Meeting. 15- 17 August.
Price, DJ, 1967. Nations can publish or perish. Science and Technology. 70: 84-90.
Thelwall M, 2012. A history of webometrics. Bulletin of the American Society for Information Science and
Technology, 38(6): 18-23.
Vinkler P. 2008. Correlation between the structure of scientific research, scientometric indicators and
GDP in EU and non-EU countries. Scientometrics, 74(2): 237-254.
Wouters P, Scharnhorst A. 2006. "Web indicators: a new generation of S&T indicators?." Cybermetrics:
International Journal of Scientometrics, Informetrics and Bibliometrics (10):7.