A measurement model for electricity Consumers' awareness with covariance structure Analyses. A solid pillar for boosting demand response programs

Recent energy policies aim to raise the awareness on climate change, fossil fuels, collective actions and social cohesion. Additionally, they seek to sensitize people to the usage of Renewable Energy Sources (RES) and rational electricity consumption. Demand Response (DR) programs are therefore significant and more present in the grid operators, aggregators and retailers' strategies to engage consumers to change their consumption behavior. Furthermore, the electricity consumers awareness and DR programs are important in the actual context of conventional resource scarcity to motivate consumers to reduce or adjust consumption and take proenvironmental measures. One of the barriers that restricts the implementation of DR programs is the consumers awareness and their perceptions about energy usage, as well as, the DR programs potential, in terms of cost savings and contribution to the reduction of environmental pollution. A DR program shows higher adoption levels if it creates positive perceptions and expectations on outcomes, leading to the conclusion that it is essential to carefully design and create DR programs. In a large-scale adoption of DR programs, we have considered consumers perceptions and expectations using first-order and hierarchical Confirmatory Factor Analysis (CFA), such as second-order and bi-factor CFA. Thus, we statistically demonstrate the influence of the electricity consumers attitude, Internet access, perceived expectations, consumer-supplier relationship, and pro-environmental measures (the latent factors) on their awareness and DR programs implementation. The simulations involve pretrial questionnaire data offered by the Irish Commission for Energy Regulation. Although many studies have been performed with this data, a measurement model for electricity consumers awareness with covariance structure analyses is an original approach that provides interesting insights. Several models are assessed here, and the best bi-factor CFA model is built providing outstanding performance results: Comparative Fit Index, CFI = 0.95 on average, Standardize Root Mean Square Residual, SRMR = 0.03, Root Mean Square Error of Approximation RMEAS = 0.02. The bi-factor CFA model provides the most interesting results and confirms the hypothesis that the five latent factors significantly impact on the consumers' awareness.