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. 2020 Dec 4;139:110578. doi: 10.1016/j.rser.2020.110578

Table A.4.

Binary logistic regression models predicting intention to purchase individual smart home technology items: solar system; electric vehicle; smart thermostat; and smart light.


Solar system
Electric vehicle
Smart thermostat
Smart light
Model D1
Model D2
Model D3
Model D4
Odds ratio (p-value)
Odds ratio (p-value)
Odds ratio (p-value)
Odds ratio (p-value)
Respondent characteristics
Female (vs. male) 0.412 (0.070) 0.765 (0.166) 0.790 (0.194) 0.962 (0.838)
Age (categories) 0.737*** (<0.001) 0.741*** (<0.001) 0.875 (0.079) 1.035 (0.669)
Bachelor's or higher (vs. less than bachelor's degree) 0.697*** (<0.001) 1.637* (0.020) 1.002 (0.992) 0.768 (0.230)
Household characteristics
Household income 1.087* (0.013) 1.074* (0.023) 1.021 (0.487) 1.017 (0.589)
Single family home 1.173 (0.448) 0.754 (0.166) 1.188 (0.367) 0.936 (0.739)
Owner occupied household 1.436 (0.109) 1.020 (0.928) 1.224 (0.315) 0.760 (0.197)
Household size 1.080 (0.356) 0.965 (0.667) 0.926 (0.326) 0.947 (0.490)
Minors present (younger than 18 years old) 1.462 (0.134) 1.541 (0.080) 1.633* (0.035) 2.026** (0.005)
Midday occupancy change (weekdays) during SIP 0.920 (0.185) 0.938 (
0.273)
1.024 (0.662) 1.076 (0.222)
Change in frequency of energy-related activities during SIP 1.011 (0.754) 1.030
0.377
1.154*** (<0.001) 1.157*** (<0.001)
Intercept 0.412 (0.070) 0.588 (0.262) 0.470 (0.101) 0.522 (0.174)
Akaike information criterion 669.97 733.12 782.35 699.42
N 662 712 641 522

Significance level: *p < 0.05; **p < 0.01; ***p < 0.001.