Ioannis C. Drivas

Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits’ metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors’ behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations.

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.

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.

This research process is focused on the analysis of three axes which are Fair Treatment, Team Effectiveness, and Job Satisfaction of employees and decisionmakers who are occupied in Nonprofit Organizations. Nowadays, the reduced financial flexibility imposes a careful delimitation of strategic communication that is implemented by Nonprofit Organizations. The aim is to examine a strategic communication process for a sustainable entrepreneurial environment. More specifically, this research attempts to find a possible correlation between the personal perception of each employee regarding the level he treated fairly in the working environment (Fair Treatment) and the existence of an effective team into which the employee feels he is a part of (Team Effectiveness). The purpose is to draw conclusions on how these two factors impact on the Job Satisfaction of the employee or decisionmaker. The possibility to have correlations among the above axes can be operated as a feedback to highlight strengths and weaknesses of Nonprofit Organizations to their decision makers.

Purpose – This paper aims to examine the Self-Other Agreement between leaders and employees in the sector of Libraries and Information Services (LIS) to construct a sustainable and strategic communicational process among library directors and staff. Design/methodology/approach – A sample of 135 leaders-employees of 17 organisations of LIS in more than five countries answered on a quantitative methodological research instrument in a multiplicity of variables. Statistical analysis of independent samples t-test was used to testify our research hypotheses. Findings – Results indicated that there is a difference in means between the two independent samples (leaders-employees). There are library leaders who rate themselves quite high, and there are employees who rate their leaders with lower evaluations. Research limitations/implications – This research extends and improves the matter of Self-Other Agreement in the sector of LIS through the collection of data that indicated a possible gap...

The commercial success of customer behavior and characteristics must be outweighed by the appropriate strategic planning. One of the most important criteria for the sustainability of a company is to minimize possibilities for making an incorrect decision. In addition, high importance shows the correct quantitatively dissemination of company resources with a vision of potential development. In this research approach there is an effort to design a dynamic simulation model. This model has been designed to minimize chances for receiving an incorrect decision, as well as the determination of channelling company resources at the right time in the right quantity creating in this way the proportional feedback resources for an organisation.

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.

Acquiring knowledge about users' opinion and what they say regarding specific features within an app, constitutes a solid steppingstone for understanding their needs and concerns. App review utilization helps project management teams to identify threads and opportunities for app software maintenance, optimization and strategic marketing purposes. Nevertheless, app user review classification for identifying valuable gems of information for app software improvement, is a complex and multidimensional issue. It requires foresight and multiple combinations of sophisticated text pre-processing, feature extraction and machine learning methods to efficiently classify app reviews into specific topics. Against this backdrop, we propose a novel feature engineering classification schema that is capable to identify more efficiently and earlier terms-words within reviews that could be classified into specific topics. For this reason, we present a novel feature extraction method, the DEVMAX.DF combined with different machine learning algorithms to propose a solution in app review classification problems. One step further, a simulation of a real case scenario takes place to validate the effectiveness of the proposed classification schema into different apps. After multiple experiments, results indicate that the proposed schema outperforms other term extraction methods such as TF.IDF and χ 2 to classify app reviews into topics. To this end, the paper contributes to the knowledge expansion of research and practitioners with the purpose to reinforce their decision-making process within the realm of app reviews utilization.

In this study the authors highlight the importance of Keywords in the process of Search Engine Optimization in an effort to increase the global ranking of websites in search engines. Through the usage of tools and indicators the authors proceed into the extraction of appropriate keywords that can be used in websites for the construction of text and content. In addition a dynamic simulation modeling process takes place in order to calculate and estimate the proper distribution of a company's resources which intends to invest in the optimization of its website for the improvement of the current presence in the digital marketing world.

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.

In this research paper, the authors highlight the importance of Search Engine Optimization of a company's website in order to improve its visibility in the global ranking of websites. Firstly the authors implement a SEO analyzing tool for the identification of rectifications that need to be done for the augmentation of website's visibility. In the next step the recommendations that SEO analyzer indicated implemented and completed improving in this way the overall SEO rating. Thereafter, a Dynamic Simulation Modeling process takes place for the estimation of the proper time and way of spending company's resources for the augmentation of website's visibility. The model predicted and estimated that the total satisfaction of a decision maker regarding this return on investment is gradually increased as each one of these recommendations implemented in a specific way of resources' distribution , strengthening the final decision in order to adopt such a digital marketing tool in decision maker's quiver.

Attracting visitors to a website is a complex and multidimensional task for each decision maker in the digital marketing sector. Even an organization in relation with its competitors holds the reins in the provision of the most qualitative products and services rather than others, the hard reality though, depicts that if the online users are not able to navigate easily on the organization’s website, they will jump to another. This fact also brings low visibility and traffic metrics on the organization’s website, which unintentionally leads to the poor communicational promotion of products and services. In this paper, the authors combine the fragmented pieces of the usability and the levels of traffic that a website has, based on the utility of the Search Engine Optimization process for improving the website’s usability and traffic as well. To this respect, the SEO process addresses and examines the website’s usability in design, architecture, and content, for improving greater volume and quality of online users’ visits to the website through search engines. Following a user-centered digital marketing approach, the authors examine, if the level of traffic of a website, related to its level of usability that expresses, based exclusively on its user’s perceptions and suggestions about that under-examined website. Implementing all user’s suggestions and thereafter, adopting Google Analytics as a web usage mining tool for measuring the optimization, the results indicate that following the website’s user’s perceptions and suggestions about it for improving its usability, the total pageviews, the organic traffic, and also the referral traffic of the website rose significantly. To this end, highlighting the utility and practicality of this paper, it is useful to refer that it could be used as a practical toolbox for each digital marketing team, in order to estimate in a well-organized and descriptive manner, the users’ perceptions as regards to a website in order to improve its usability levels and thus its traffic.