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| 1 |
ID:
127235
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| Publication |
2014.
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| Summary/Abstract |
We focus on predicting the adoption time probabilities of photo-voltaic solar panels by households using discrete choice experiments and an innovation diffusion model. The primary objective of this research is cohesively mapping the theory of disruptive innovation into diffusion of innovations to aid policy makers by linking two critical uncertainties of new technology: (1) whether households prefer the new attributes of the new technology and how these preferences vary by market segments? and (2) when are they going to adopt (if at all)? Our study uses recent developments of discrete choice experiments and establishes a causal link between the attributes of the technology, attitudinal constructs and socio-demographics, and adoption time probabilities using the Bass diffusion model. The data was collected from Ontario, a province of Canada. The innovation diffusion model allows us to compute the cumulative probability of adoption over time per household. Technology awareness and energy cost saving have a significant effect on the adoption probability, reinforcing the need for effective education. These findings also suggest that campaigns should explain more about investment criteria, feed-in tariffs and environmental attributes. This study findings call for a need to use seeding strategies to accelerate exogenous Word-of-Mouth (WOM) for this new technology.
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| 2 |
ID:
119827
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| Publication |
2013.
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| Summary/Abstract |
We are concerned with micro-generation, individual households generating electricity using a renewable technology. We focus on modeling the adoption probability of photo-voltaic solar panels by households. Using data from Ontario, Canada where a generous feed-in-tariff is available to households generating electricity from solar panels, we measure household level preferences for panels and use these preferences along with household characteristics to predict adoption time intentions. We use discrete choice experiments to measure household level preferences and establish a causal link between the attributes of the technology and adoption time intentions using discrete time survival mixture analysis. Significant preferences included lower cost, greater energy savings and lower fossil fuel inflation. The conditional (hazard) probability of adoption at a particular time given no previous adoption showed that the attribute preferences had intuitively reasonable effects. The hazard probabilities allow us to compute the cumulative probability of adoption over a 10-year period per household. Technology awareness has a significant effect on the adoption probability, reinforcing the need for effective education. Our approach indicates the level of heterogeneity in preferences, particularly high for investment criteria and CO2 emissions. These findings suggest that education campaigns should explain more about investment criteria, feed-in tariffs and environmental effects.
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