Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

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.

University archives: the research road travelled and the one ahead

“…Using two different search engines (Google and Altavista), the authors identified the proper content curation and specific metadata aspects as factors affecting UA ranking in search engines. Their preliminary assumptions were confirmed in a more sophisticated way by a series of recent research efforts (Drivas et al, 2021;V allez and Ventura, 2020;Alhuay-Quispe, 2017).…”

Posicionamiento orgánico en motores de búsqueda, su relevancia científica y tendencias de investigación

“…2022) detallan la pertinencia incluir herramientas de minería de texto, para determinar el efecto de las estrategias de marketing digital en la intención de compra de los consumidores. Otras investigaciones indican la necesidad de analizar la digitalización de organizaciones culturales como bibliotecas, archivos y museos, dado que estos modelos de negocio tienen características particulares como la protección de propiedad intelectual (Drivas et al, 2021; requiere adelantar una segmentación de usuarios culturales, desde un enfoque cualitativo, que permita incluir dimensiones emocionales como la satisfacción.…”

Data Curation and Optimization Techniques: A Systematic Mapping Study

“…This classification is available (in tabular form) at shorturl.at/D1234. [24], [15] [18], [17], [20], [22], [25], Techniques [27], [28], [43], [64], [30], [32], [34], [38], [40], [45], [46], [47], [49], [54], [55], [56], [57], [59], [60], [61], [66], [68], [69], [70], [73], [74], [75], [77], [26], [42], [44], [11], [13], [36]. [12], [14], [16], [19], [21], [23], [29], [31], [35], [39], [41], …”