Indicative Publications

@article{nokey,

title = {Unveiling the feed: Academic libraries' instagram unpacked},

author = {Ioannis C Drivas and Eftichia Vraimaki},

doi = {https://doi.org/10.1016/j.acalib.2024.102924},

year  = {2024},

date = {2024-07-06},

urldate = {2024-07-06},

journal = {The Journal of Academic Librarianship},

volume = {50},

issue = {5},

abstract = {In the ever-evolving social media landscape, Instagram has transcended from a mere image-sharing platform to a dynamic space for academic libraries to engage with their communities. Following the increased utilization of this platform, several studies have tried to unravel the interplay between nuanced content aspects and follower engagement, but the results are cursory and contradicting. Aiming to address these shortcomings, we conducted an in-depth analysis of 1681 posts from 120 academic libraries' Instagram profiles worldwide to explore the following: content volume and posting frequency; qualitative content aspects such as post categories, characters length, hashtags usage, emojis frequency, and post types; and possible correlations between these content aspects and follower post interaction rates. Our findings uncovered notable disparities in interaction rates among 14 distinct post categories, with content structure characteristics showing minimal influence on these rates. By shedding light on the association between aspects of content structure and follower interaction, the study contributes to the development and optimization of academic libraries' social media strategy, policy redefinition, staff knowledge and practical skills improvement to manage social media, while also opening new research avenues in Instagram utilization in the academic library context.},

keywords = {Academic libraries, analytics, behavioral analytics, social media},

pubstate = {published},

tppubtype = {article}

}

@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}

}