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Web Analytics for Higher Education Institution Websites in India: Need of the Hour


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1 Associate Professor, Centre for Digital Innovation, FORE School of Management, New Delhi, India
     

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Top ranking higher education institutions in India have a cent percent digital presence in maintaining their website. On the maturity stage model, these websites are present at all stages, mainly on interaction and transaction levels (Tripathi, 2018). With a rise in online communication, it is crucial to analyse its performance through web analytics. This paper attempts to identify the key performance indicators for websites of higher education institutions in India and suggest specific web analytic tools for each stage of the maturity model. This study uses data from the author(s) published study where content analysis has been used for the front end. A conceptual framework has been developed for higher education institutions where web analytics is applied to each stage of the maturity model.



Keywords

Higher Education, Maturity Models, Information, Interaction, Transaction, Integration, Web Analytics, Key Performance Indicators.
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  • Al-Debei, M. M. (2014). The quality and acceptance of websites: An empirical investigation in the context of higher education. International Journal of Business Information Systems, 15(2), 170–188.

  • Alhomod, S. M., Shafi, M. M., Kousarrizi, M. N., Seiti, F., Teshnehlab, M., Susanto, H., Batawi, Y. A. (2012). Best practices in E-government: A review of some innovative models proposed in different Countries. International Journal of Electrical & Computer Sciences, 12(01), 1–6.

  • Almahamid, S. M., Tweiqat, A. F. and Almanaseer, M.S. (2016). University website quality characteristics and success: Lecturers' perspective. International Journal of Business Information Systems, 22(1), 41–61.

  • Al-Mukhaini, E. M., Al-Qayoudhi, W. S. and Al-Badi, A. H. (2014). Adoption of social networking in education: A study of the use of social networks by higher education students in Oman. Journal of International Education Research, 10(2), 143–153.

  • Arksey H, O'Malley L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1),19–32.

  • Brown, A., & Green, T. (2012). Issues and trends in instructional technology: Lean times, shifts in online learning, and increased attention to mobile devices. In M. Orey, S. A. Jones, & R. M. Branch (Eds.), Educational media and technology yearbook: Volume 36 (pp. 67-80). Springer. 10.1007/978-1-46141305-9_6

  • Buccoliero, L., & Bellio, E. (2010). Citizens web empowerment in European municipalities. Journal of E-Governance, 33(4), 225–236.

  • Chaffey, D. and Patron, M. (2012). From web analytics to digital marketing optimization: Increasing the commercial value of digital analytics. Journal of Direct, Data and Digital Marketing Practice, 14(1), 30-45.

  • Chandler, A., Morris, M., & Wallace, M. (2016, April 4). Cornell University Library is Migrating from Google Analytics to Piwik:Why and How [Conference presentation]. Electronic Resources and Libraries Conference, Austin, United States.

  • Chawinga, W. D. and Zinn, S. (2016). Use of Web 2.0 by students in the faculty of information science and communications at Mzuzu University, Malawi. Journal of Information Management, 18(1), 1–12.

  • Chen, L. (2013). University students information literacy education based on the Web 2.0 environment. Management and Engineering, 13(5), 23–38.

  • Cheung, C. M., Chiu, P. Y. and Lee, M. K. (2011). Online social networks: Why do students use Facebook?. Computers in Human Behaviour, 27(4), 1337–1343.

  • Clifton, B. (2012). Advanced web metrics with Google Analytics, John Wiley & Sons.

  • Deventer, M., & Lues, H. (2019). Factors influencing generation Y students' university website usage intentions: A case of selected South African universities. Journal of Contemporary Management, 16(1), 255-271.

  • Kaushik, A. (2009). Web analytics 2.0: The art of online accountability and science of customer centricity. John Wiley & Sons.

  • Kim, D., Kim, J. H. and Nam, Y. (2014). How does industry use social networking sites? An analysis of corporate dialogic uses of Facebook, Twitter, YouTube, and LinkedIn by industry type. Quality & Quantity, 48(5), 2605-2614.

  • Kim, D. Y. and Grant, G. (2010). E-government maturity model using the capability maturity model integration. Journal of Systems and Information Technology, 12(3), 230–244.

  • Khopkar, C., Vasilik, K. E., Lin, Z., Bardin, A., & Nelson, D. A. (2014). US Patent No. 8,682,712. Washington, DC: US Patent and Trademark Office.

  • Kompen, R. T., Edirisingha, P., Canaleta, X., Alsina, M., & Monguet, J. M. (2019). Personal learning Environments based on Web 2.0 services in higher education. Telematics and Informatics, 38, 194206. 10.1016/j.tele.2018.10.003

  • Layne, K. and Lee, J. (2001). Developing fully functional e-Government: A four-stage model. Government Information Quarterly, 18(2), 122-136.

  • Lee, J. (2010). 10 year retrospect on stage models of e-Government: A qualitative meta-quarterly, 27(3), 220-230.

  • Loftus, W. (2012). Demonstrating success: Web analytics and continuous improvement. Journal of Web Librarianship, 6(1), 45-55. 10.1080/19322909.2012.651416

  • Moon, M. J. (2002). The Evolution of E-Government among Municipalities: Rhetoric or Reality?. Public administration review, 62(4). 424-433.

  • Peterson, E. T. (2004). Web analytics demystified: A marketer's guide to understanding how your web site affects your business. Celilo Group Media.

  • Rodden, Kerry, Hilary Hutchinson, and Xin Fu. (2010). Measuring the user experience on a large scale: User-centered metrics for web applications. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2395-2398. 10.1145/1753326.1753687.

  • Ronaghan, S. A. (2001). Benchmarking E-Government: A Global Perspective. New York: United Nations Division for Public Economics and Public Administration and American Society for Public Administration. http://unpan1.un.org

  • Romanowski, B., & Konak, A. (2016). Using Google Analytics to Improve the Course Website of a Database Course. In ASEE Mid Atlantic Regional Conference Papers. October 21-22, 2016.

  • Saura, J. R., Palos-Sánchez, P., & Cerdá Suárez, L. M. (2017). Understanding the digital marketing environment with KPIs and web analytics. Future Internet, 9(4), 76.

  • Schimmel, K., Motley, D., Racic, S., Marco, G., & Eschenfelder, M. (2010). The importance of university web pages in selecting a higher education institution. Research in Higher Education Journal, 9, 1. https://www.aabri.com/manuscripts/10560.pdf.

  • Sheu, F. R., & Shih, M. (2017). Evaluating NTU's opencourseware project with google analytics: User characteristics, course preferences, and usage patterns. The International Review of Research in Open and Distributed Learning, 18(4). 10.19173/irrodl.v18i4.3025

  • Sorum, H., Andersen, K. N., and Clemmensen, T.(2013). Website quality in government: Exploring the webmaster's perception and explanation of website quality. Transforming Government: People, Process and Policy, 7(3),322-341.

  • Sukumaran, A. K. S. (2015). End user computing satisfaction instrument for a university website in India. International Journal of Business Information Systems, 20(4), 496–508.

  • Tripathi, R., & Gupta, M. P. (2014). Evolution of government portals in India: mapping over stage models. Journal of Enterprise Information Management, 27(4), 449-474.

  • Tripathi, R. (2017). Web analytics for each stage of E-government implementation: A study in the Indian context. Journal of e-Government Studies and Best Practices, 2017. 10.5171/2017.295851

  • Tripathi, R. (2018). From information to interaction: Website and social media usage and trends in top Indian higher education institutions. International Journal of Business Information Systems, 29(2), 139-154.

  • Tripathi, Rakhi. (2020). Evolution of higher education institution websites in India: A longitudinal study. International Journal of Management Concepts and Philosophy, 13(4), 319-331.

  • Tucciarone, Krista M. (2009). Speaking the same language: Information college seekers look for on a college website. College & University, 84(4), 22-31.

  • Verborgh, R. and De Wilde, M. (2013). Using Open Refine. Packt Publishing Ltd. Wagner, N., Hassanein, K., & Head, M. (2008). Who is responsible for e-learning success in higher education? A stakeholders' analysis. Journal of Educational Technology & Society, 11(3), 26-36.

  • Waisberg, D. and Kaushik, A. (2009). Web analytics 2.0: Empowering customer centricity. The Original Search Engine Marketing Journal, 2(1), 5-11.

  • Web Analytics Association (2008). Web analytics definitions. https://www.webanalytiker.dk/wpcontent/ logo/blog/WAA-Standards-Analytics-Definitions.pdf

  • West, D. M. (2005). Global E-Government. http://www.insidepolitics.org/egovt05int.pdf. Accessed June 28, 2020.


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  • Web Analytics for Higher Education Institution Websites in India: Need of the Hour

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Authors

Rakhi Tripathi
Associate Professor, Centre for Digital Innovation, FORE School of Management, New Delhi, India

Abstract


Top ranking higher education institutions in India have a cent percent digital presence in maintaining their website. On the maturity stage model, these websites are present at all stages, mainly on interaction and transaction levels (Tripathi, 2018). With a rise in online communication, it is crucial to analyse its performance through web analytics. This paper attempts to identify the key performance indicators for websites of higher education institutions in India and suggest specific web analytic tools for each stage of the maturity model. This study uses data from the author(s) published study where content analysis has been used for the front end. A conceptual framework has been developed for higher education institutions where web analytics is applied to each stage of the maturity model.



Keywords


Higher Education, Maturity Models, Information, Interaction, Transaction, Integration, Web Analytics, Key Performance Indicators.

References