## 3 Materials and Methods
Thus, first of all, the aim was to classify the respondents into different consumption profiles, taking into account the following facilitating conditions:  
* The residential environment, taking into account the differences regarding the consumer behaviors of users in urban areas compared to rural areas; it was considered, on the one hand, that there is wider accessibility of EESS technologies in urban areas (ex. faster and more stable connections to the Internet, easier integration into hubs, higher e-readiness, etc. ), and on the other hand, that there are a greater number of options for setting up smart home systems that integrate EESS in rural areas. * The type of housing, a factor that correlates with the residence environment, taking into account the fact that, in general, the urban population lives in apartments, while the rural population generally lives in homes/villas. This factor influences both the variety of EESS solutions available depending on each type of home, as well as the need to streamline energy consumption, taking into account different energy consumptions depending on the type and size of the home. * The form of ownership (family owned or rented), a factor that influences respondents' willingness to invest in home improvement through the integration of EESS technologies. * The average monthly electricity consumption, which on the one hand, indicates the potential yield that could be obtained through the integration of EESS, and on the other hand, reflects the pressure that respondents feel in order to reduce energy costs. In Romania, for example, users with consumptions higher than 300 kWh/month (the consumption ceiling for subsidized energy prices in 2022) are more likely to adopt energy efficiency measures. * The number of respondents who changed their supplier in the last year, an indicator that reflects one of the major trends in the sector in Romania--citizens' concern in the direction of optimizing electricity costs. For example, the year 2022 in Romania marked a significant increase in the market share of energy suppliers that offered lower prices. Last but not least, in the category of facilitating conditions, two criteria that correlated with the possibility of subsidizing the purchase of EESS-like solutions were included, given that one of the objectives of the research is precisely that of providing proposals for improving public policies in the field of energy efficiency. Thus, the respondents were asked; one, whether they benefited from government subsidies for the price of electricity; and two, whether they benefited from subsidies for the purchase of EESS technologies. Data were processed with the structural equation modeling technique (PLS-SEM) and the SmartPLS 4 application [50]. More specifically, PLS-SEM was used to specify and evaluate the measurement model and structural model, and to test the research hypotheses. The relationships between the variables included in the model were analyzed both through the lens of direct effects and indirect (mediated) effects. The specific peculiarities of the evaluation of the PLS-SEM model and the results obtained are described in Section 4, and indicate a significantly different impact of the two dimensions of social influence (SI_FF and SI_PS) on the other variables analyzed.