Document Type : Articles
Authors
1 Department of Computer, College of Mechatronic, Karaj Branch, Islamic Azad University, Alborz, Iran
2 Department of Computer, College of Mechatronic, Karaj Branch, Islamic Azad University, Alborz, Iran.
Abstract
The purpose of this study is to analyse the correlation between content and traffic of 21,485 academic websites (universities and research institutes). The achieved result is used as an indicator which shows the performance of the websites for attracting more visitors. This inspires a best practice for developing new websites or promoting the traffic of the existing websites. At the first step, content of the site is divided into three major items which are: Size, Papers and Rich Files. Then, the Spearman correlation between traffic of the websites and these items are calculated for each country and for the world, respectively. At the next step, countries are ranked based on their correlations, also a new indicator is proposed from combining these three correlations of the countries. Results show that in most countries, correlation between traffic of the websites and Papers is less than correlations between traffic of the websites and Rich Files and Size.
- Aguillo, I. F. (2012). Is Google Scholar useful for bibliometrics? A webometric analysis. Scientometrics, 91(2), 343-351.
- Aguillo, I. F., Bar-Ilan, J., Levene, M., & Ortega, J. L. (2010). Comparing university rankings. Scientometrics, 85(1), 243-256.
- Aguillo, I. F., Granadino, B., Ortega, J. L., & Prieto, J. A. (2006). Scientific research activity and communication measured with cybermetrics indicators. Journal of the American Society for information science and technology, 57(10), 1296-1302.
- Alexa the web information company. (2013). About. Retrieved from: http://www.alexa.com/company.
- Asher, A. D., Duke, L. M., & Wilson, S. (2012). Paths of discovery: comparing the search effectiveness of EBSCO Discovery Service, Summon, Google Scholar, and conventional library resources. College & Research Libraries, crl-374.
- Blanco-Ramirez, G., & Berger, J. B. (2014). Rankings, accreditation, and the international quest for quality: Organizing an approach to value in higher education. Quality Assurance in Education, 22(1), 88-104.
- Booth, D., & Jansen, B. J. (2009). A review of methodologies for analyzing websites.Handbook of Research on Web Log Analysis. Information Science Reference, 143-164.
- deMoya-Anegon, F., Lopez-Illescas, C., & Moed, H. F. (2014). How to interpret the position of private sector institutions in bibliometric rankings of research institutions.Scientometrics, 98(1), 283-298.
- Harzing, A. W. (2013). A preliminary test of Google Scholar as a source for citation data: a longitudinal study of Nobel prize winners. Scientometrics, 94(3), 1057-1075.
- Jamaludin, A. (2013, December). The Influence of Website Trust and Loyalty on Customer Intention to Purchase Online. In 4th International Conference on Business and Economic Research (4th Icber 2013) Proceeding.
- Lavhengwa, T. J., Lavhengwa, E. M., & van der Walt, J. S. A Collaboration Index for Research Institutions.
- Lee, J., Min, J. K., Oh, A., & Chung, C. W. (2014). Effective ranking and search techniques for Web resources considering semantic relationships. Information Processing & Management, 50(1), 132-155.
- Lo, W. Y. W. (2014). Dimension 3: University Rankings and the Global Landscape of Higher Education: Using University Rankings to Promote Local Interests. In University Rankings (pp. 119-137). Springer Singapore.
- Meier, J. J., & Conkling, T. W. (2008). Google Scholar’s coverage of the engineering literature: an empirical study. The Journal of Academic Librarianship, 34(3), 196-201.
- Mishra, M. R. (2014). Web Usage Mining Contextual Factor: Human Information Behavior. International Journal of Information Technology and Management.
- Nalini, T., & Sangeetha, G. (2014). A Survey of Information Retrieval in Web Mining. Middle-East Journal of Scientific Research, 19(8), 1123-1126.
- Orduña-Malea, E., & Regazzi, J. J. (2014). US academic libraries: understanding their web presence and their relationship with economic indicators. Scientometrics, 98(1), 315-336.
- Ortega, J. L., & Aguillo, I. (2010). Differences between web sessions according to the origin of their visits. Journal of Informetrics, 4(3), 331-337.
- Ortega, J. L., & Aguillo, I. F. (2008). Visualization of the Nordic academic web: Link analysis using social network tools. Information Processing & Management, 44(4), 1624-1633.
- Peterson, K. (2013). Academic web site design and academic templates: Where does the library fit in?. Information Technology and Libraries, 25(4), 217-221.
- Thelwall, M., & Sud, P. (2011). A comparison of methods for collecting web citation data for academic organizations. Journal of the American Society for Information Science and Technology, 62(8), 1488-1497.
- Thelwall, M., & Harries, G. (2004). Do the web sites of higher rated scholars have significantly more online impact?. Journal of the American Society for Information Science and Technology, 55(2), 149-159.
- Vaughan, L., Yang, R. (2013). Web traffic and organization performance measures: Relationships and data sources examined. Journal of Informetrics, 7(3), 699–711.
- Weth, C. V. D., & Hauswirth, M. (2013, November). DOBBS: Towards a Comprehensive Dataset to Study the Browsing Behavior of Online Users. In Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on (Vol. 1, pp. 51-56). IEEE.
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