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THE WEB ANALYTICS FRAMEWORK AS AN INTEGRATED USER-CENTRIC EVALUATIVE TOOL

This paper presents a conceptual framework that integrates web analytics techniques with other evaluative tools to provide rich insights for the improvement of information systems. It emphasizes user-centricity and the utilization of big data.

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THE WEB ANALYTICS FRAMEWORK AS AN INTEGRATED USER-CENTRIC EVALUATIVE TOOL

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  1. THE WEB ANALYTICS FRAMEWORK AS AN INTEGRATED USER-CENTRIC EVALUATIVE TOOL Presenter: Ejidokun, A.O. Co-authors: Akhigbe, B.I., Aderibigbe, S.O., Afolabi, B.S. & Adagunodo E.R. Transition from Obeservation to Knoweldege to Intelligence (TOKI) University of Lagos, Nigeria 2016

  2. INTRODUCTION • I&AS? • Systems include Decision Support System, Collaborative systems. Co-operative systems etc. Smart city systems include intelligent transport management, smart electricity management, automated building management, assisted living and e-health management (Moreno et al., 2015) • Web Analytics Framework? Pitfalls? (Fagan,2014)

  3. INTRODUCTION CONTD. • Integrated user-centric evaluative tool? • User-centricity? • This paper presents an evaluative framework because emphasis should be developed to serve the user and not the demonstration of the use of specific technology, nor the skillful use of an elegant piece of programming (Gulliksen et al., 2003)

  4. LITERATURE REVIEW Waisberg and Kushik (2009) presented a Web analytics process that can be followed as a hands-on process. As a holistic approach, its use for Website analysis is based on several sources of knowledge. This possibility made it plausible to incorporate the Big Data Integrative Module (BDIM) that can use the Big Data Architecture (BDA) as sources of knowledge Phippen et al. (2004) applied the Web analytics technique for the effective evaluation of online strategies towards e-Commerce success. Fagan (2014) leverage on Web analytics data to examine user behaviour. The work highlighted the plausibility in the need to explore the potentials of the Web analytics for user-centric evaluation.

  5. JUSTIFICATION • The potentials of Web analytics as underscored in literature (Chen et al., 2012), leaves researchers with little or no choice to want to adapt its. • The current Web analytics encompass social search and mining, reputation systems, social media analytics, and Web visualization capability. • In addition, some of the other promising research directions that is related to Web analytics include Web-based auctions, Internet monetization, social marketing, and Web privacy/security (Chen et al., 2012). • In the health sector, the technique of Web analytics has been used to examine online public reports of quality. Researchers often used key performance index like how visitors find reports and the purpose of users visit (Bardach et al., 2015). • Thus, based on user-centric conceptualizations, the Web analytics techniques were used to improve search engine traffic, cost information and Website experience for both consumers and health care professionals (Bardach et al., 2015). • In this paper, what is done differently from the work reviewed so far, is the synergistic combination of the Web analytic technique with other useful evaluative tools

  6. THEORETICAL FRAMEWORK • Human Computer Interaction (HCI) theoretics • Theory of Information Processing (TIP) (Gao et al., 2012) • Unconscious Thought Theory (UTT)

  7. THEORETICAL FRAMEWORK • The component: • Web Analytics technique (WAt), • the Knowledge base (KbHCI), and • the Big Data Integrative Module (BDIM) are the specific • The goal: • is the provision of rich evaluation findings that can be used to improve I&AS and other computing artifacts when properly contextualized.

  8. THE PROPOSED FRAMEWORK Figure 1: The proposed framework

  9. CONCLUSION The plausibility of the proposed framework • Is based on the potential to deliver parsimonious evaluative user-centric models, and evaluative results that can inform the development of more engaging information systems that are I&AS. • The framework provides conceptual information that can stimulate further debate in the domain of user-centred evaluative research. • The framework is novel: • Its components - KbHCI and BDIM - though exist in literature (Fischer, 2001; Bilal et al. 2016) – • have not been leveraged nor so suggested for user-centric evaluative practices with the WAt as postulated in this paper. However: the proposed framework is conceptual; it needs to be tested. • The postulations provided are subject to further research and empirical testing.

  10. REFERENCES • Bilal, M., Oyedele, L.O., Akinade, O.O., Ajayi, S.O., Alaka, H.A., Owolabi, H.A., ..., and Bello, S.A. (2016). Big Data Architecture for ConstructingWasteAnalytics: A Conceptualframework. Journal of Building Engineering. Retrievedfromhttp://dx.doi.org/ 10.10 16/j.jobe.2016.03.002 on 30/03/2016 @ 19:38 pm • Bardach, N.S., Hibbard, J.H., Greaves, F., and Dudley, R.A. (2015). Sources of Traffic and Visitors’ PreferencesRegarding Online Public Reports of Quality: Web Analytics and Online Survey Results. Journal of Medical Internet Research, 17(5).e102. • Chen, H., Chiang, R.H., and Storey, V.C. (2012). Business Intelligence and Analytics: FromBig Data to Big Impact. MIS Quarterly, 36(4), 1165-1188. • Fagan, J.C. (2014). The Suitability of Web Analytics Key Performance Indicators in the Academic Library Environment, Journal of Academic Librarianship 40, 25–34. • Gao, J., Zhang, C., Wang, K., and Ba, S. (2012). Understanding Online Purchase Decision Making: The Effects of Unconscious Thought, Information Quality, and Information Quantity. Decision Support Systems, 53(4), 772-781.

  11. REFERENCES • Gulliksen, J., Goransson, B., Boivie, I., Blomkvist, S., Persson, J., and Cajander, A. (2003). Key Principles for User-centredSystems Design. Behaviour and Information Technology, 22(6), 397-409 • Harpur, P. A., and de Villiers, R. (2015). MUUX-E, a Framework of Criteria for Evaluating the Usability, User Experience and Educational Features of m-Learning Environments. South African Computer Journal, 56(1), 1-21. • Moreno, M.V., Zamora, M.A., and Skarmeta, A.F. (2015). An IoTBased Framework for User–centric Smart Building Services. International Journal of Web and Grid Services, 11(1), 78-101. • Phippen, A., Sheppard, L., and Furnell, S. (2004). A Practical Evaluation of Web Analytics. Internet Research, 14(4), 284- 293. • Waisberg, D., and Kaushik, A. (2009). Web Analytics 2.0: Empowering Customer Centricity. The Original Search Engine Marketing Journal, 2(1), 5-11.

  12. THANKS FOR LISTENING COMMENTS & QUESTIONS?

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