## 2 Conceptual Model and Research Hypotheses
### Conceptual Model
A study by [PERSON] and [PERSON] [22] in the field of using TAM for the analysis of acceptance of energy technologies shows that social influence has the highest impact on perceived usefulness (PU), attitude, perceived ease of use (PEOU), behavioral intention (BI), and actual use. [PERSON] and [PERSON] [13] show the important role of social image in the early stages of adopting smart connected objects (SCO) as well as the positive influence on PU and PEOU. [PERSON] and [PERSON] [26] show the influence of subjective norms on both the attitude toward use and the behavioral intention to use. Other studies [35] approached social influence from the perspective of social networks, demonstrating the positive impact of social media features (SMF) above perceived usefulness (PU) and perceived ease of use (PEOU). The correlation between social influence and PU is also highlighted in the research of [PERSON], [PERSON], and [PERSON] [36]. [PERSON] et al. [12] shows that social norms have a strong impact on the behavioral intention to use HEMS technologies in Japan and the USA. However, another study [37] shows, that social influence does not have a significant effect on the behavioral intention to use artificial intelligence (AI)-based technologies, in the context of the still insufficient level of maturity of these solutions. This hypothesis is also supported by [PERSON] et al. [38], in a TAM-based research on the adoption of autonomous vehicles (AVs). Based on the models analyzed, this research proposes a conceptual model that includes seven main constructs and eight hypotheses (Figure 1). One of the main features of the proposed conceptual model, which differentiates it from previous applications of the TAM model in the EESS technologies sector, is that of a differentiated approach to social influence on these dimensions: network of friends, colleagues, or family (SI_FF), and public space (SI_PS). This approach is justified, as shown previously, by a number of specificities of EESS solutions compared to other new technologies. In fact, some authors [5] consider that in the case of IT&C, the acceptance of  
Figure 1: Conceptual model and research hypotheses. new technologies is strongly influenced by the adoption at the level of the social network, and in the case of SET technologies, this correlation is less strong; it is considered that social influence should be exercised at public level, through media institutions. The stronger the social influence, the more inclined the individual is to engage in energy-saving actions [39]. A difference in the prospects of colleagues or friends can be a barrier to the adoption of such technologies [39]. On the other hand, social networks are a favorable environment for the personalized exercise of social influence, as it is a factor that emphasizes the role of social influence [40], while influence maximization (IM) becomes an important concept in both the analysis of social networks [41] and the optimization of marketing strategies to promote EESS technologies. Social influence is also a determinant that stimulates the network effect in adopting EESS. Research [9] showed that people with positive experiences using smart home technologies will recommend them to friends and family or promote them through social networks. In a study in Malaysia [19], it is shown that campaigns on environmental protection and the use of green technologies should be extended to social networks. The results of the proposed conceptual model are the behavior intention to use (BIOU). In conformity with the theory of planned behavior [42], at the individual level, behavioral intentions represent a result of attitude, subjective norms, and perceived behavioral control. BIOU is also a main construct of the UTAUT and UTAUT2 models, which condition use behavior.