Why are hyperlinks to business Websites created? A content analysis

Motivations for the creation of hyperlinks to business sites were analyzed through a content analysis approach. Links to 280 North American IT companies (71 Canadian companies and 209 U.S. companies) were searched through Yahoo!. Then a random sample of 808 links was taken from the links retrieved. The content as well as the context of each link was manually examined to determine why the link was created. The country location and the type of the site where the link came from were also identified. The study found that most links were created for business purposes confirming findings from early quantitative studies that links contain useful business information. Links to competitors were extremely rare but competitors were often co-linked, suggesting that co-link analysis is the direction to pursue for information on competitive intelligence. Copyright © 2006 Akadémiai Kiadó, Budapest. All rights reserved

Una introducción a la investigación webmétrica de empresas.

La Web ha experimentado un enorme desarrollo en los últimos 20 años. La Webmetría se presenta como una nueva disciplina que pretende estudiar desde un punto de vista cuantitativo este nuevo canal de comunicación mediante la adaptación y aplicación de técnicas bibliométricas. Este artículo aborda la relevancia de los hiperenlaces como fuente de información y explica brevemente las técnicas más prometedoras que pueden emplearse para obtener datos acerca de fenómenos que se manifiestan online. Hasta el momento los estudios en este campo se han centrado en el estudio de espacios académicos, sin embargo esta metodología se puede aplicar igualmente a sitios comerciales, que son los que dominan la Web. El artículo revisa algunos estudios que han descubierto que el número de enlaces que apuntan a páginas web de empresas se correlacionan significativamente con determinadas medidas de desempeño financiero. Este descubrimiento sugiere que los enlaces entrantes en un sitio web podrían emplearse como un indicador adicional del éxito de las actividades empresariales. Los trabajos realizados hasta el momento se se han limitado a un determinado sector empresarial, el de tecnologías de la información, y a un número reducido de países (Estados Unidos, Canadá y China). Es preciso llevar a cabo más investigación de carácter exploratorio con el fin de valorar las posibilidades de futuros desarrollos en esta área de investigación. The Web has undergone an enormous development in the last 20 years. Webometrics is a new discipline that applies Bibliometric techniques to study the Web from a quantitative approach. This paper addresses the relevance of hyperlinks as a source of information, and it explains briefly the most promising techniques that can be used to mine data about online phenomena. So far studies in this field have focused on academic spaces, however this methodology is equally applicable to commercial sites which dominate the Web. This paper reviews studies that found that the number of links pointing to company websites correlates significantly with some company´s business performance measures. This finding may imply that links to a website could be used as an additional indicator of business performance. Nevertheless this research has some limitations such as the focus on the IT industry and on a limited amount of countries (e.g. USA, Canada and China). More exploratory research is needed in order to assess possible future developments in this area.webmetría, minería de datos en la web, rendimiento empresarial, ratios. webometrics, web data mining, business performance, ratios.

Revealing the online network between university and industry: the case of Turkey

The present paper attempts to explore the relationship between the Turkish academic and industry systems by mapping the relationships under web indicators. We used the top 100 Turkish universities and the top 10 Turkish companies in 10 industrial sectors in order to observe the performance of web impact indicators. Total page count metric is obtained through Google Turkey and the pure link metrics have been gathered from Open Site Explorer. The indicators obtained both for web presence and web visibility indicated that there are significant differences between the group of academic institutions and those related to companies within the web space of Turkey. However, this current study is exploratory and should be replicated with a larger sample of both Turkish universities and companies in each sector. Likewise, a longitudinal study rather than sectional would eliminate or smooth fluctuations of web data (especially URL mentions) as a more adequate understanding of the relations between Turkish institutions, and their web impact, is reached.Orduña Malea, E.; Aytac, S. (2015). Revealing the online network between university and industry: the case of Turkey. Scientometrics. 105(3):1849-1866. doi:10.1007/s11192-015-1596-4S184918661053Aguillo, 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.Arslan, M. L., & Seker, S. E. (2014). Web based reputation index of Turkish Universities. International Journal of E-Education E-Business E-Management and E-Learning, 4(3), 197–203.Aytac, S. (2010). International scholarly collaboration in science, technology and medicine and social science of Turkish scientists. The International Information & Library Review, 42(4), 227–241.Bahçıvan, E. (ed.) (2013). Turkey’s Top 500 Industrial enterprises 2012. The Journal of the Istanbul Chamber of Industry, 48(569), 1–124 (special issue).Barabasi, A. L., & Albert, R. (1999). Emergence of Scaling in Random Networks. Science, 286(5439), 509–512.Cankir, B., Arslan, M. L., & Seker, S. E. (2015). Web Reputation Index for XU030 Quote Companies. Journal of Industrial and Intelligent Information, 3(2), 110–113.Faba-Fernández, C., Guerrero-Bote, Vicente P., & Moya-Anegón, F. (2003). Data mining in a closed web environment. Scientometrics, 58(3), 623–640.Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force‐directed placement. Software: Practice and experience, 21(11), 1129–1164.García-Santiago, L., & De Moya-Anegón, F. (2009). Using co-outlinks to mine heterogeneous networks. Scientometrics, 79(3), 681–702.Jolliffe, I. (2002). Principal component analysis. New York: Springer.Khan, G. F., & Park, H. W. (2011). Measuring the triple helix on the web: Longitudinal trends in the university–industry–government relationship in Korea. Journal of the American Society for Information Science and Technology, 62(12), 2443–2455.Leydesdorff, L., & Etzkowitz, H. (1996). Emergence of a triple helix of university–industry–government relations. Science and public policy, 23(5), 279–286.Leydesdorff, L., & Park, H. W. (2014). Can synergy in triple-helix relations be quantified? A review of the development of the triple-helix indicator. arXiv preprint arXiv:1401.2342.Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49(1), 93–123.Meyer, M., Siniläinen, T., & Utecht, J. T. (2003). Towards hybrid triple helix indicators: A study of university-related patents and a survey of academic inventors. Scientometrics, 58(2), 321–350.Minguillo, D., & Thelwall, M. (2012). Mapping the network structure of science parks: An exploratory study of cross-sectoral interactions reflected on the web. Aslib Proceedings, 64(4), 332–357.Montesinos, P., Carot, J. M., Martínez, J. M., & Mora, F. (2008). Third mission ranking for world class universities: Beyond teaching and research. Higher Education in Europe, 33(2/3), 259–271.Orduna-Malea, E., & López-Cózar, E. D. (2014). Google scholar metrics evolution: An analysis according to languages. Scientometrics, 98(3), 2353–2367.Ortega, J. L., Orduna-Malea, E., & Aguillo, I. F. (2014). Are web mentions accurate substitutes for inlinks for Spanish universities? Online Information Review, 38(1), 59–77.Priem, J., & Hemminger, B. H. (2010). Scientometrics 2.0: New metrics of scholarly impact on the social Web. First Monday, 15(7). http://firstmonday.org/ojs/index.php/fm/article/viewArticle/2874 . Accessed 31 December 2014.Romero-Frías, E. (2011). Googling companies-a webometric approach to business studies. Leading Issues in Business Research Methods, 7(1), 93–106.Romero-Frías, E., & Vaughan, L. (2010). Patterns of web linking to heterogeneous groups of companies: The case of stock exchange indexes. Aslib Proceedings, 62(2), 144–164.Stuart, D., & Thelwall, M. (2006). Investigating triple helix relationships using URL citations: A case study of the UK West Midlands automobile industry. Research Evaluation, 15(2), 97–106.Thelwall, M. (2004). Link analysis: An information science approach. San Diego: Academic Press.Thelwall, M. (2014). A brief history of altmetrics. Research trends, 37, 3–4. http://www.researchtrends.com/issue-37-june-2014/a-brief-history-of-altmetrics/ . Accessed 31 December 2014.Thelwall, M., & Harries, G. (2003). The connection between the research of a university and counts of links to its Web pages: An investigation based upon a classification of the relationships of pages to the research of the host university. Journal of the American Society for Information Science and Technology, 54(7), 594–602.Vaughan, L. (2004). Exploring website features for business information. Scientometrics, 61(3), 467–477.Vaughan, L. (2006). Visualizing linguistic and cultural differences using Web co-link data. Journal of the American Society for Information Science and Technology, 57(9), 1178–1193.Vaughan, L., & Yang, R. (2012). Web data as academic and business quality estimates: A comparison of three data sources. Journal of the American Society for Information Science and Technology, 63(10), 1960–1972.Vaughan, L., Gao, Y., & Kipp, M. (2006). Why are hyperlinks to business Websites created? A content analysis. Scientometrics, 67(2), 291–300.Vaughan, L., & Romero-Frías, E. (2012). Exploring web keyword analysis as an alternative to link analysis: A multi-industry case. Scientometrics, 93(1), 217–232.Vaughan, L., & Thelwall, M. (2003). Scholarly use of the web: What are the key inducers of links to journal web sites? Journal of the American Society for Information Science and Technology, 54(1), 29–38.Vaughan, L., & Wu, G. (2004). Links to commercial web sites as a source of business information. Scientometrics, 60(3), 487–496.Wilkinson, D., & Thelwall, M. (2013). Search markets and search results: The case of Bing. Library & Information Science Research, 35(4), 318–325

Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites

"This article is (c) Emerald Group Publishing and permission has been granted for this version to appear here https://doi.org/10.1108/AJIM-10-2017-0226. Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited"[EN] Purpose - The purpose of this paper is to determine the effect of the chosen search engine results page (SERP) on the website-specific hit count estimation indicator. Design/methodology/approach - A sample of 100 Spanish rare disease association websites is analysed, obtaining the website-specific hit count estimation for the first and last SERPs in two search engines (Google and Bing) at two different periods in time (2016 and 2017). Findings - It has been empirically demonstrated that there are differences between the number of hits returned on the first and last SERP in both Google and Bing. These differences are significant when they exceed a threshold value on the first SERP. Research limitations/implications - Future studies considering other samples, more SERPs and generating different queries other than website page count (ositeW) would be desirable to draw more general conclusions on the nature of quantitative data provided by general search engines. Practical implications - Selecting a wrong SERP to calculate some metrics (in this case, website-specific hit count estimation) might provide misleading results, comparisons and performance rankings. The empirical data suggest that the first SERP captures the differences between websites better because it has a greater discriminating power and is more appropriate for webometric longitudinal studies. Social implications - The findings allow improving future quantitative webometric analyses based on website-specific hit count estimation metrics in general search engines. Originality/value - The website-specific hit count estimation variability between SERPs has been empirically analysed, considering two different search engines (Google and Bing), a set of 100 websites focussed on a similar market (Spanish rare diseases associations), and two annual samples, making this study the most exhaustive on this issue to date.Font-Julian, CI.; Ontalba Ruipérez, JA.; Orduña Malea, E. (2018). Hit count estimate variability for website-specific queries in search engines: The case for rare disease association websites. Aslib Journal of Information Management. 70(2):192-213. https://doi.org/10.1108/AJIM-10-2017-0226S192213702Bar-Ilan, J. (2001). Scientometrics, 50(1), 7-32. doi:10.1023/a:1005682102768Bowler, L., Hong, W., & He, D. (2011). The visibility of health web portals for teens: a hyperlink analysis. Online Information Review, 35(3), 443-470. doi:10.1108/14684521111151469European Organization for Rare Diseases (2012), “What is a rare disease?”, available at: www.eurordis.org/content/what-rare-disease (accessed 10 January 2018).Forman, J., Taruscio, D., Llera, V. A., Barrera, L. A., Coté, T. R., … Edfjäll, C. (2012). The need for worldwide policy and action plans for rare diseases. Acta Paediatrica, 101(8), 805-807. doi:10.1111/j.1651-2227.2012.02705.xGao, Y., & Vaughan, L. (2005). Web hyperlink profiles of news sites. Aslib Proceedings, 57(5), 398-411. doi:10.1108/00012530510621851Gouveia, F. C., & Kurtenbach, E. (2009). Mapping the web relations of science centres and museums from Latin America. Scientometrics, 79(3), 491-505. doi:10.1007/s11192-007-1949-8Groselj, D. (2014). A webometric analysis of online health information: sponsorship, platform type and link structures. Online Information Review, 38(2), 209-231. doi:10.1108/oir-01-2013-0011Lewandowski, D. (2008). A three-year study on the freshness of web search engine databases. Journal of Information Science, 34(6), 817-831. doi:10.1177/0165551508089396Li, X. (2003). A review of the development and application of the Web impact factor. Online Information Review, 27(6), 407-417. doi:10.1108/14684520310510046Noruzi, A. (2006). The web impact factor: a critical review. The Electronic Library, 24(4), 490-500. doi:10.1108/02640470610689188Orduna-Malea, E. (2014), “Caracterización y rendimiento del sistema museístico de la comunidad valenciana a través de un análisis cibermétrico”, in Gimenez-Chornet, V. (Ed.), Gestión Cultural: Innovación y Tendencias, Tirant Lo Blanch, Valencia, pp. 13-43.Orduña-Malea, E., Delgado López-Cózar, E., Serrano-Cobos, J., & Romero, N. L. (2015). Disclosing the network structure of private companies on the web. Online Information Review, 39(3), 360-382. doi:10.1108/oir-11-2014-0282Park, H. W., Kim, C.-S., & Barnett, G. A. (2004). Socio-Communicational Structure among Political Actors on the Web in South Korea. New Media & Society, 6(3), 403-423. doi:10.1177/1461444804042522Rodríguez i Gairín, J. M. (1997). Valoración del impacto de la información en Internet: Altavista, el «Citation Index» de la red. Revista española de Documentación Científica, 20(2), 175-181. doi:10.3989/redc.1997.v20.i2.591Romero-Frías, E., & Vaughan, L. (2010). European political trends viewed through patterns of Web linking. Journal of the American Society for Information Science and Technology, 61(10), 2109-2121. doi:10.1002/asi.21375Satoh, K. and Yamana, H. (2012), “Hit count reliability: how much can we trust hit counts?”, in Sheng, Q.Z., Wang, G., Jensen, C.S. and Xu, G. (Eds), Asia-Pacific Web Conference, Springer, Berlin Heidelberg, April, pp. 751-758.Snyder, H., & Rosenbaum, H. (1999). Can search engines be used as tools for web‐link analysis? A critical view. Journal of Documentation, 55(4), 375-384. doi:10.1108/eum0000000007151Uyar, A. (2009). Investigation of the accuracy of search engine hit counts. Journal of Information Science, 35(4), 469-480. doi:10.1177/0165551509103598Vaughan, L., & Thelwall, M. (2004). Search engine coverage bias: evidence and possible causes. Information Processing & Management, 40(4), 693-707. doi:10.1016/s0306-4573(03)00063-3Vaughan, L., & Wu, G. (2004). Links to commercial websites as a source of business information. Scientometrics, 60(3), 487-496. doi:10.1023/b:scie.0000034389.14825.bcWilkinson, D., & Thelwall, M. (2013). Search markets and search results: The case of Bing. Library & Information Science Research, 35(4), 318-325. doi:10.1016/j.lisr.2013.04.00

Disclosing the network structure of private companies on the web: the case of Spanish IBEX 35 share index

[EN] Purpose - It is common for an international company to have different brands, products or services, information for investors, a corporate blog, affiliates, branches in different countries, etc. If all these contents appear as independent additional web domains (AWDs), the company should be represented on the web by all these web domains, since many of these AWDs may acquire remarkable performance that could mask or distort the real web performance of the company, affecting therefore on the understanding of web metrics. The purpose of this paper is to determine the amount, type, web impact and topology of the AWDs in commercial companies in order to get a better understanding on their complete web impact and structure. Design/methodology/approach - The set of companies belonging to the Spanish IBEX-35 stock index has been analysed as testing bench. The authors proceeded to identify and categorise all AWDs belonging to these companies, and to apply both web impact (web presence and visibility) and network metrics. Findings - The results show that AWDs get a high web presence but relatively low web visibility, due to certain opacity or less dissemination of some AWDs favoring its isolation. This is verified by the low network density values obtained, that occur because AWDs are strongly connected with the corporate domain (although asymmetrically), but very weakly linked each other. Research limitations/implications - The categories used to classify the various AWD, although they are clearly distinguishable conceptually, have certain limitations in practice, since they depend on the form adopted by companies to publish certain content or to provide certain services or products. Otherwise, the use of web indicators presents certain problems of accuracy that could be softened if applied with caution and in a relational basis. Originality/value - Although the processes of AWDs creation and categorisation are complex (web policy seems not to be driven by a defined or conscious plan), their influence on the web performance of IBEX 35 companies is meaningful. This research measures the AWDs influence on companies under webometric terms for the first time.This research has been funded under the project APOSTD/2013/002 from the Regional Ministry of Education, Culture and Sport (Generalitat Valenciana) in Spain.Orduña Malea, E.; Delgado López-Cózar, E.; Serrano-Cobos, J.; Lloret Romero, MN. (2015). Disclosing the network structure of private companies on the web: the case of Spanish IBEX 35 share index. Online Information Review. 39(3):360-382. https://doi.org/10.1108/OIR-11-2014-0282S36038239

Una introducción a la investigación webmétrica de empresas

La Web ha experimentado un enorme desarrollo en los últimos 20 años. La Webmetría se presenta como una nueva disciplina que pretende estudiar desde un punto de vista cuantitativo este nuevo canal de comunicación mediante la adaptación y aplicación de técnicas bibliométricas. Este artículo aborda la relevancia de los hiperenlaces como fuente de información y explica brevemente las técnicas más prometedoras que pueden emplearse para obtener datos acerca de fenómenos que se manifiestan online. Hasta el momento los estudios en este campo se han centrado en el estudio de espacios académicos, sin embargo esta metodología se puede aplicar igualmente a sitios comerciales, que son los que dominan la Web. El artículo revisa algunos estudios que han descubierto que el número de enlaces que apuntan a páginas web de empresas se correlacionan significativamente con determinadas medidas de desempeño financiero. Este descubrimiento sugiere que los enlaces entrantes en un sitio web podrían emplearse como un indicador adicional del éxito de las actividades empresariales. Los trabajos realizados hasta el momento se se han limitado a un determinado sector empresarial, el de tecnologías de la información, y a un número reducido de países (Estados Unidos, Canadá y China). Es preciso llevar a cabo más investigación de carácter exploratorio con el fin de valorar las posibilidades de futuros desarrollos en esta área de investigación.The Web has undergone an enormous development in the last 20 years. Webometrics is a new discipline that applies Bibliometric techniques to study the Web from a quantitative approach. This paper addresses the relevance of hyperlinks as a source of information, and it explains briefly the most promising techniques that can be used to mine data about online phenomena. So far studies in this field have focused on academic spaces, however this methodology is equally applicable to commercial sites which dominate the Web. This paper reviews studies that found that the number of links pointing to company websites correlates significantly with some company´s business performance measures. This finding may imply that links to a website could be used as an additional indicator of business performance. Nevertheless this research has some limitations such as the focus on the IT industry and on a limited amount of countries (e.g. USA, Canada and China). More exploratory research is needed in order to assess possible future developments in this area