Knowing and understanding cultural heritage in digital environments: An approach using MIMIC and network models

Abstract

Knowing and understanding cultural heritage is essential for proper value-attribution, since without historical, social, political, economic or artistic contexts, we cannot attribute value to it. Knowledge, which is the first phase of the Heritage Learning Sequence (HLS), enables us to identify the causes and justifications that explain its nature and state, and provides a sound grounding for heritage valuation. The dimensions knowing and understanding, as measured by the Q-Herilearn scale (Fontal, Ibañez-Etxeberria, et al., 2024b) in digital environments have been analysed according to the answers given by a sample of 2362 participants aged 18 to 70. Comparative analyses between groups (frequentist and Bayesian) have been carried out, the validity of both the measurement models and the structural model (MIMIC) has been determined, and the analyses were complemented by means of network analysis. Both the measurement model and the final structural model (MIMIC with DIF) have provided sufficient guarantees in terms of validity and reliability, and results have been endorsed by network analysis. The dimensions analysed (knowledge and understanding of heritage) are strongly interconnected, so that the understanding of heritage depends largely on the degree of prior knowledge. However, we have found no evidence (or very weak, given the small effect size) of the influence of socio-demographic variables on either the dimensions or the indicators that measure them. We believe that the most relevant contribution of this research is the combination of structural equation-based models with network analysis-based models to study the knowledge and understanding of cultural heritage in digital contexts.