Indicative Publications

@article{nokey,

title = {Speed matters: What to prioritize in optimization for faster websites},

author = {Ioannis C Drivas and Xilogianni Christina and Filippos Doukas and Dimitrios Kouis},

doi = {https://doi.org/10.3390/analytics1020012},

year  = {2022},

date = {2022-11-25},

urldate = {2022-11-25},

journal = {Analytics},

volume = {1},

issue = {2},

pages = {175-192},

abstract = {Website loading speed time matters when it comes to users’ engagement and conversion rate optimization. The websites of libraries, archives, and museums (LAMs) are not an exception to this assumption. In this research paper, we propose a methodological assessment schema to evaluate the LAMs webpages’ speed performance for a greater usability and navigability. The proposed methodology is composed of three different stages. First, the retrieval of the LAMs webpages’ speed data is taking place. A sample of 121 cases of LAMs worldwide has been collected using the PageSpeed Insights tool of Google for their mobile and desktop performance. In the second stage, a statistical reliability and validity analysis takes place to propose a speed performance measurement system whose metrics express an internal cohesion and consistency. One step further, in the third stage, several predictive regression models are developed to discover which of the involved metrics impact mostly the total speed score of mobile or desktop versions of the examined webpages. The proposed methodology and the study’s results could be helpful for LAMs administrators to set a data-driven framework of prioritization regarding the rectifications that need to be implemented for the optimized loading speed time of the webpages.},

keywords = {analytics, libraries, museums, performance, web analytics, website},

pubstate = {published},

tppubtype = {article}

}

@article{Drivas2022,

title = {Social Media Analytics and Metrics for Improving Users Engagement},

author = {Drivas, Ioannis and Kouis, Dimitrios and Kyriaki-Manessi, Daphne and Giannakopoulou, Fani},

editor = {MDPI},

url = {https://www.mdpi.com/2673-9585/2/2/14},

doi = {https://doi.org/10.3390/knowledge2020014},

isbn = {2673-9585},

year  = {2022},

date = {2022-05-12},

urldate = {2022-05-12},

journal = {Knowledge},

volume = {2},

issue = {2},

pages = {18},

abstract = {Social media platforms can be used as a tool to expand awareness and the consideration of cultural heritage organizations and their activities in the digital world. These platforms produce daily behavioral analytical data that could be exploited by the administrators of libraries, archives and museums (LAMs) to improve users’ engagement with the provided published content. There are multiple papers regarding social media utilization for improving LAMs’ visibility of their activities on the Web. Nevertheless, there are no prior efforts to support social media analytics to improve users’ engagement with the content that LAMs post to social network platforms. In this paper, we propose a data-driven methodology that is capable of (a) providing a reliable assessment schema regarding LAMs Facebook performance page that involves several variables, (b) examining a more extended set of LAMs social media pages compared to other prior investigations with limited samples as case studies, and (c) understanding which are the administrators’ actions that increase the engagement of users. The results of this study constitute a solid stepping-stone both for practitioners and researchers, as the proposed methods rely on data-driven approaches for expanding the visibility of LAMs services on the Social Web.},

keywords = {analytics, behavioral analytics, facebook, social data analysis, social media, social networks, users interaction, web analytics},

pubstate = {published},

tppubtype = {article}

}

@article{Drivas2021b,

title = {Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology},

author = {Ioannis C Drivas and Dimitris Kouis and Daphne Kyriaki-Manessi and Georgios Giannakopoulos},

url = {https://www.mdpi.com/2078-2489/12/7/259},

doi = {https://doi.org/10.3390/info12070259},

year  = {2021},

date = {2021-06-24},

journal = {Information},

volume = {12},

number = {7},

pages = {18},

abstract = {While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.},

keywords = {archives, content management systems, libraries, museums, search engine optimization, web analytics},

pubstate = {published},

tppubtype = {article}

}

@article{Drivas2020c,

title = {Big Data Analytics for Search Engine Optimization},

author = {Ioannis C Drivas and Daphne Kyriaki-Manessi and Giannakopoulos Georgios and Damianos P Sakas},

url = {https://www.mdpi.com/2504-2289/4/2/5},

doi = {https://doi.org/10.3390/bdcc4020005},

year  = {2020},

date = {2020-04-02},

journal = {Big Data and Cognitive Computing},

volume = {4},

number = {2},

pages = {23},

abstract = {In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web.},

keywords = {Big data, cultural institutions, search engine optimization, web analytics},

pubstate = {published},

tppubtype = {article}

}