This quantitative analysis was performed on survey data in four phases. First, descriptive statistics were examined to show the demographic makeup of the respondents. Second, descriptive statistics were calculated on the ratings of the decision factors that influenced participant adoption of residential solar systems. Third, correlations were calculated to identify key relationships between demographic characteristics and ratings of the decision factors. Finally, the importance ratings were grouped by the year of solar system installation to evaluate change in importance of factors over time. Each of the ten decision factors that were rated in importance to the solar power system adoption were put in ordinal format of integers from 1 (not important at all) to 5 (extremely important). The minimum, maximum, mean, and median values were calculated for each for the entire sample. For this analysis, the key determinant of importance was the mean score.
4.3. Decision Factors
With the ordinal value assigned to the importance of each factor for each survey response, the mean value of each decision factor was calculated. These are displayed graphically in Figure 1
. The most important overall factor in the decision to adopt solar power systems was that solar power has a positive impact on the environment (μ = 4.25). This was followed closely by the expectation of a reduction in energy bills (μ = 4.18). This strikes a nearly fair balance between environmental and economic considerations, with the environment rated as slightly more important. These two factors are considered the highest tier factors in importance.
The factors in the next tier of importance were perceived honesty of the solar sales representative (μ = 3.90) and reduced dependency on the power company (μ = 3.89). The next tier was comprised of leaving a positive legacy (μ = 3.59) and reputation of the solar installer (μ = 3.58). The factors fall off in importance after that, with recommendation for solar power from peers being the least important factor (μ = 2.30).
In addition to the 10 specific decision factors for which the survey collected importance ratings, respondents were given the opportunity to list another non-listed decision factor of their own choosing. Respondents who chose to do this wrote in a description of the other decision factor and provided an importance rating.
Approximately 17% of respondents (307 of 1842) indicated another
factor. For those who did this, the other decision factor descriptions varied widely, and many of those descriptions were exact matches to or closely related to the ten decision factors listed in the survey. The 307 other responses had a high mean importance score of 4.08. This leads to the inference that the category was used by some respondents to emphasize specific decision factors. The other factors were grouped into three categories: solar economics, environmental impact, and undefined. These were coded based on the text provided by respondents indicating certain themes, which were generally either cost (economic) related or environmental benefits related. Examples of the economics category include “good investment” and “tax breaks”. Examples of the environmental impact category include “global warming” and “environmental”. The unspecified factors were diverse and generally unfit for analysis. Examples include “I signed the loan agreement the day Donald Trump was elected” and “retired”. Table 3
shows the number of other factors in each of these categories and the mean importance rating of each.
In a follow-up question, survey participants were asked to select the top three most important decision factors. Participant responses were summed for each decision factor. The top two most common selections were also the two decision factors with the highest mean importance ratings. The first and second decision factors are reversed when compared to the mean of the importance ratings. These are shown in Table 4
Expected energy bill reduction and positive impact on the environment are far more important than the other factors based on this second approach. Most of the factors had similar importance between both approaches to measuring importance, with one key exception. In the overall ratings, low or no up-front cost rated eighth. However, in the top three approach, it ranked as the third most selected decision factor.
shows importance ratings by gender and indicates a fundamental difference between the male and female respondents. Females in the sample rate positive impact on the environment as the most important decision factor. Men in the sample rate expected energy bill reduction as the most important decision factor. This difference was not explored beyond the descriptive statistics presented here but represents a key indication for future research.
In addition to identifying the most important decision factors, this analysis was designed to calculate correlations between demographic parameters and the importance of decision factors. Table 6
shows the correlation coefficient ρ-values between four demographic factors (age, education, home value, and income) and the importance assigned to the decision factors. The table also shows the number of responses in each calculation (N) and the statistical significance calculated with the two-tailed t-test all produced by SPSS.
Interpreting this table relies on an understanding of both correlation and significance. There were 40 variables tested for correlation. Of the 40 variables tested, 25 were shown to be statistically significant. However, the correlation coefficients were low, ranging from −0.178 to 0.210. While there is no universal criterion that assigns a label of weak or strong to a correlation factor value, the closer to zero that the coefficient is, the weaker the correlation. This table suggests significant but weak correlations, suggesting that the differences among respondents in terms of education, age, income, and home value and their responses regarding motivational factors may be statistically but not meaningfully significant. These data suggest that prioritizing low or no up-front cost is significantly and negatively correlated with all four demographic factors, that expected energy bill reduction is significantly and negatively correlated with education, that valuing the positive impact on the environment is most strongly, significantly, and positively correlated with education, and that recommendations from peers and perceived reputation of solar installer result in the lowest and least significant correlations.
4.4. Decision Factors over Time
The wealth of data from the large number of survey responses enabled this analysis to consider how importance of decision factors has evolved over time. The study horizon considered the five-year period from 2013 through 2017. Table 7
shows the average importance rating of each decision factor for each year of installation represented by the sample.
There are three key takeaways from this data. First, positive impact on the environment
and expected energy bill reduction
maintained their respective number one and number two ratings throughout the study period. Second, the ratings for most decision factors remained relatively flat over the study horizon. Third, two of the less important decision factors over the entire period showed the largest change between 2013 and 2017. Recommendations for solar power from peers
and reputation of my solar installer
were the fastest growing decision factors in terms of importance as shown in the shaded area of the Change
column of Table 7
. These results make sense, given that as adoption increases in prevalence and visibility, discussions with peers about solar adoption become more likely and reputations of solar installers likely become more established and well known.