*4.4. The Effect of the Participation in the Study on Energy Monitoring and Attitude towards Environmental Issues*

In the next step, we conducted three exploratory Principal Component Analyses (PCA), one for each point of measurement T0 (*n* = 274), T4 (*n* = 145), T5 (*n* = 142), for questions A1–A6 (pro-enviromental attitudes), B1–B5 (monitoring behaviors), and M1–M16 (attitudes towards monitoring) with the exclusion of items M1, M2, and M10 (see Table A1 in the Appendix A for a description of variables, their coding and scales used in the study). We excluded these items because they were referring to energy monitoring and environment protection at the same time, which caused ambiguity we wanted to avoid. Altogether, we included 24 items in conducted PCAs.

The results of Bartlett sphericity tests and KMO coefficients indicated that a reduction of dimensions may be useful with collected data (see Table 4 for details). We used eigenvalues above 1 as a criterion to select the number of components. In effect, for each measurement (T0, T4, and T5), the solution with two components best fitted the data. We based our selection of items for each component on item loading cut-off point, which was set to 0.3.


**Table 4.** Coefficients of Bartlett sphericity tests, KMO, eigenvalues, and percentage of explained variance for solutions with two components.

Note: *d f* for Bartlett sphericity test are based upon the number of variables included in the analysis.

The first component was **energy monitoring (EM)**, which contained the following items: B1–B5, M4, M5, M6, M7, M8, M9, M11, M12, M13, M14, M15, and M16. This component explained respectively 32.62% (T0), 34.75% (T4), and 39.59% (T5) of variance. The exemplary items that best describe this component are: "I decided to use internet platforms/applications to monitor energy consumption in my household" (M11), "I check monthly energy consumption according to data from the electricity meter" (B2), "I believe that energy monitoring is good" (M9), "I feel bad when I don't control the energy consumption in my household" (M7).

In the second component, **attitude towards environmental issues (EA)**, we included items number A1, A2R, A3, A4, A5R, A6 (R—means negative loading). This component explained respectively 13.62% (T0), 11.87% (T4) and 10.66% (T5) of variance. The items that best describe this factor are: "In my opinion, reports about the ecological crisis are exaggerated" (A2R), "I am happy when the climate and environment protection plays an important role in politics" (A3), "In my opinion, every person has an impact on environmental protection through his own behavior" (A4), "Protecting the environment is particularly important to me (A1).

After exploring the results of PCA, we decided to remove item M3 from further analyses as it was causing problems with coherent components' interpretation. In T0, item M3 was loading the second component but did not suit it from the semantic point of view. In T4, component loading of M3 did not exceed 0.3, and, in T5, it was loading the first component. There was a small variance of item loadings between each point of measurement, hence we decided to apply the same two component solutions for each measurement.

Finally, we created two factors from 23 items and each factor was produced by calculating arithmetic mean scores, where high scores mean more favorable attitude towards environmental issues and more endorsement of energy monitoring. The reliability of these two components was examined using the Cronbach's alpha. Cronbach's alpha for each component at each time of measurement was at least on the level of 0.65. Internal reliability for monitoring of energy consumption was *α* = 0.90 (T0), *α* = 0.92 (T4), *α* = 0.93 (T5) and for a pro-environmental attitude was *α* = 0.65 (T0), *α* = 0.77 (T4), *α* = 0.74 (T5). These results indicate an acceptable consistency of the measurement items and construct reliability. Some more descriptive statistics and normality test for EM and EA at three points of measurement T0, T4, and T5 are presented in Appendix A in Table A2.

To explore the effect of the participation in the study on the energy monitoring and attitude towards environmental issues, we conducted a repeated measures ANOVAs with the group variable as a between group factor and time of measurement of energy monitoring as a dependent variable measured at T0, T4, and T5 (*n* = 142). The results of the analysis showed a statistically significant main effect of the time of measurement for energy monitoring, F(1.72, 242.09) = 14.74, *p* < 0.001, partial-*η*<sup>2</sup> = 10% see Table 5. The results of a post-hoc pairwise comparison with Sidak correction revealed that participants energy monitoring at T4 (M = 3.30, SD = 0.72) was significantly higher than in T0 (M = 3.16, SD = 0.71), - = 0.14, *p* = 0.009, and the energy monitoring at T5 (M = 3.39; SD = 0.75) was significantly higher than at T0 and T4, respectively - = 0.23, *p* < 0.001 and - = 0.09, *p* = 0.020. This outcome means that participants' energy Monitoring (EM) was increasing with each point of measurement. We also performed the same analysis with the attitude towards environmental issues (EA). However, we found no significant effects of participation in the study on participants' attitudes towards environmental issues (see Table 5 and Figure 3).


**Table 5.** Results of the repeated measures ANOVA for energy monitoring (EM) and attitude towards environmental issues (EA).

**Figure 3.** Mean scores with SE for repeated measurements of energy monitoring (EM) and attitude towards environmental issues (EA).

#### *4.5. Knowledge and Education as Correlates of Energy Monitoring and Attitude towards Environmental Issues*

In the last analysis, we explored relationships between energy monitoring (EM), attitude towards environmental issues (EA), and knowledge measured at T0, T4, and T5, as well as education level. To measure knowledge, we asked four questions (K1–K4) testing participant's familiarity with the following terms and issues: (K1) the concept of smart grid; (K2) the concept of smart metering; (K3) the opportunity to change the energy supplier; and (K4) the most energy-consuming home appliance. Each question had only one correct answer, so the sum of the collect answers might have ranged from 0 to 4. In the T5 point of measurement, the majority of respondents knew which of the home appliances is the most energy-intensive (91.5% correct answers). In addition, most of the respondents (83%) were aware that SM enables remote reading of energy consumption by the energy supplier. Less respondents were aware of who may change the electricity supplier or what smart grid means (62.7% and 30% of the correct answers, respectively).

We used the Spearman correlation coefficient as it is less susceptible to extreme cases, and allows for assessing the relationship for ordinal data (see Table 6). The results of the conducted analyses showed that energy monitoring (EM) at T0 was moderately negatively correlated with education level and positively correlated with knowledge at T0 and T4. Energy monitoring in T4 and T5 was positively correlated with knowledge in T0, T4, and T5. Surprisingly, we found no correlations with attitude towards environmental issues and knowledge or education.


**Table 6.** Correlation analysis coefficients for relationships between energy monitoring (EM), attitude towards environmental issues (EA), Education, and Knowledge in T0, T4, T5, and weekly attitude.
