Information Organizations and their Websites Performance. A Global Report for Summarization and Optimization Purposes - Version 0.9.6
Creators
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Ioannis C. Drivas1
- Sotiris Christodoulopoulos1
- Filippos-Rafail Doukas1
- Athena Georgaraki1
- Sofia Georgiadi1
- Luiza Gjolaj1
- Florinda Kapllani1
- Vaia Ketsati1
- Chrysanthi Leotsakou1
- Lazarela Michali1
- Theano Mina1
- Alexia-Sofia Moraitou1
- Alexandra Nikitarakou1
- Alexandra Nomismatidou1
- Athina Nikolaou1
- Panagiota Patikopoulou1
- Athina Stefanidaki1
- Aggelos Sioros1
- Antonis Tataridas1
- Christina Xilogianni1
- 1. Research Lab of Information Management. Department of Archival, Library Science & Information Studies. University of West Attica
Description
Notice: You can check the new version 0.9.6 at the official page of Information Management Lab and at the Google Data Studio as well.
Description of the Report and Topic Justification:
Now that the ICTs have matured, Information Organizations such as Libraries, Archives and Museums, also known as LAMs, proceed into the utilization of web technologies that are capable to expand the visibility and findability of their content. Within the current flourishing era of the semantic web, LAMs have voluminous amounts of web-based collections that are presented and digitally preserved through their websites. However, prior efforts indicate that LAMs suffer from fragmentation regarding the determination of well-informed strategies for improving the visibility and findability of their content on the Web (Vállez and Ventura, 2020; Krstić and Masliković, 2019; Voorbij, 2010). Several reasons related to this drawback. As such, administrators’ lack of data analytics competency in extracting and utilizing technical and behavioral datasets for improving visibility and awareness from analytics platforms; the difficulties in understanding web metrics that integrated into performance measurement systems; and hence the reduced capabilities in defining key performance indicators for greater usability, visibility, and awareness.
In this enriched and updated technical report, the authors proceed into an examination of 504 unique websites of Libraries, Archives and Museums from all over the world. It is noted that the current report has been expanded by up to 14,81% of the prior one Version 0.9.5 of 439 domains examinations. The report aims to visualize the performance of the websites in terms of technical aspects such as their adequacy to metadata description of their content and collections, their loading speed, and security. This constitutes an important stepping-stone for optimization, as the higher the alignment with the technical compliencies, the greater the users’ behavior and usability within the examined websites, and thus their findability and visibility level in search engines (Drivas et al. 2020; Mavridis and Symeonidis 2015; Agarwal et al. 2012).
One step further, within this version, we include behavioral analytics about users engagement with the content of the LAMs websites. More specifically, web analytics metrics are included such as Visit Duration, Pages per Visit, and Bounce Rates for 121 domains. We also include web analytics regarding the channels that these websites acquire their users, such as Direct traffic, Search Engines, Referral, Social Media, Email, and Display Advertising. SimilarWeb API was used to gather web data about the involved metrics.
In the first pages of this report, general information is presented regarding the names of the examined organizations. This also includes their type, their geographical location, information about the adopted Content Management Systems (CMSs), and web server software types of integration per website. Furthermore, several other data are visualized related to the size of the examined Information Organizations in terms of the number of unique webpages within a website, the number of images, internal and external links and so on.
Moreover, as a team, we proceed into the development of several factors that are capable to quantify the performance of websites. Reliability analysis takes place for measuring the internal consistency and discriminant validity of the proposed factors and their included variables. For testing the reliability, cohesion, and consistency of the included metrics, Cronbach’s Alpha (a), McDonald’s ω and Guttman λ-2 and λ-6 are used.
- For Cronbach’s, a range of .550 up to .750 indicates an acceptable level of reliability and .800 or higher a very good level (Ursachi, Horodnic, and Zait, 2015).
- McDonald’s ω indicator has the advantage to measure the strength of the association between the proposed variables. More specifically, the closer to .999 the higher the strength association between the variables and vice versa (Şimşek and Noyan, 2013).
- Gutman’s λ-2 and λ-6 work verifiably to Cronbach’s a as they estimate the trustworthiness of variance of the gathered web analytics metrics. Low values less than .450 indicate high bias among the harvested web metrics, while values higher than .600 and above increase the trustworthiness of the sample (Callender and Osburn, 1979).
-Kaiser–Meyer–Olkin (KMO) and Bartlett’s Test of Sphericity indicators are used for measuring the cohesion of the involved metrics. KMO and Bartlett’s test indicates that the closer the value is to .999 amongst the involved items, the higher the cohesion and consistency of them for potential categorization (Dziuban and Shirkey, 1974).
Both descriptive statistics and reliability analyses were performed via JASP 0.14.1.0 software.
To this end, this report contributes to the knowledge expansion of all the interest parties and stakeholders related to the research topic of improving the visibility and findability of LAMs and their content on the Web. It constitutes a well-informed compass, that could be adopted by such organizations, in order to implement potential strategies that combine both domain knowledge and data-driven culture in terms of awareness optimization on the internet realm.
About the Project Team:
The whole project is managed and optimized on a weekly basis by a big young and smiley team of scientists (alphabetically referred in the next section). All of them are undergraduate students at the Department of Archival, Library and Information Studies of the University of West Attica.
They are responsible for the overall process of publishing the Technical Report which includes the initial organizations’ identification, and subsequently, websites testing, data gathering, curation and pre-processing, analysis, validation and visualization. Of course, the Team will continue to expand the capabilities of this report while involving new features, metrics, and further information regarding Libraries, Archives and Museums websites from all over the world.
Notes
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Information Organizations and their Websites Performance. A Global Report for Summarization and Optimization Purposes..pdf
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Additional details
References
- Agarwal, S., Nishar, D. and Rubin, A.E., Google LLC, 2012. Providing digital content based on expected user behavior. U.S. Patent 8,271,413
- Callender, J. C., and Osburn, H. G. (1979). An empirical comparison of coefficient alpha, Guttman's lambda-2, and MSPLIT maximized split-half reliability estimates. Journal of Educational Measurement, 89-99.
- Drivas, I. C., Sakas, D. P., Giannakopoulos, G. A., and Kyriaki-Manessi, D. (2020). Big data analytics for search engine optimization. Big Data and Cognitive Computing, 4(2), 5.
- Dziuban, C. D., and Shirkey, E. C. (1974). When is a correlation matrix appropriate for factor analysis? Some decision rules. Psychological bulletin, 81(6), 358.
- Krstić, N., and Masliković, D. (2019). Pain points of cultural institutions in search visibility: the case of Serbia. Library Hi Tech.
- Mavridis, T., and Symeonidis, A. L. (2015). Identifying valid search engine ranking factors in a Web 2.0 and Web 3.0 context for building efficient SEO mechanisms. Engineering Applications of Artificial Intelligence, 41, 75-91.
- Salminen, J., Corporan, J., Marttila, R., Salenius, T., and Jansen, B. J. (2019, March). Using Machine Learning to Predict Ranking of Webpages in the Gift Industry: Factors for Search-Engine Optimization. In Proceedings of the 9th International Conference on Information Systems and Technologies (pp. 1-8).
- Shapiro, S. S., and Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611.
- Şimşek, G. G., and Noyan, F. (2013). McDonald's ωt, Cronbach's α, and generalized θ for composite reliability of common factors structures. Communications in Statistics-Simulation and Computation, 42(9), 2008-2025.
- Ursachi, G., Horodnic, I. A., and Zait, A. (2015). How reliable are measurement scales? External factors with indirect influence on reliability estimators. Procedia Economics and Finance, 20, 679-686.
- Voorbij, H. (2010). The use of web statistics in cultural heritage institutions. Performance Measurement and Metrics.