Web-based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers' Quality of Experience (QoE). Though models exist that assess the QoE of Web Application, significant challenges remain in: (a) Defining QoE factors from a Web engineering perspective; (b) quantifying the relationship between so called 'objective' and 'subjective' factors of relevance; and (c) dealing with limited data available in relation to subjective factors. In response, the work here presents a novel model (and associated software instantiation) that integrates factors through Key Performance Indicators (KPI) and Key Quality Indicators (KQI). The mapping is incorporated into a correlation model that assesses the QoE of Web Applications, with a consideration of defining the factors in term of quality requirements derived from web architecture. The data resulting from the mapping is used as input of the proposed model to develop artefacts that quantify and predict QoE using Machine Learning (ML). The development of proposed model is framed and guided by Design Science Research DSR approach with the purpose of enabling providers to more informed decisions regarding QoE and/or to optimise resources accordingly. Though the work is oriented at developing an artefact that has clear utility for practice, the nascent design theory underpinning the work is developed and discussed.