Behavioral Decision Theory

The effectiveness of deployment policies to promote the uptake of residential rooftop solar photovoltaic systems ultimately hinges on the behaviour of households who decide to accept or reject the technology. Over the past years, research has strived to understand, designate significant predictors, model the behaviour of heterogeneous households, and predict the diffusion rate by putting a wide range of approaches in place. Inspired by compiling a comprehensive database of home solar adoption studies, the present study systematically reviews the adopted theories, methods and approaches used within 199 original quantitative, qualitative, statistical, and non-statistical articles covering households' attitudes, awareness, tendencies, knowledge, motives, willingness, intentions, and adoption decisions. The study provides a critical analysis of investigations on the adoption of solar photovoltaics, solar home systems, and solar photovoltaics coupled with battery energy storage systems. The outcome of the review revealed 108 future and 91 retroactive studies that exploit 10 key dependent variables by means of 52% primary (empirical data), 34% secondary (available sources), and 13% covering both modes of data collection. The sensitivity of these dependent variables was tested by 36 intervention variables that seek to capacitate effective managerial policies. The complexity of the individual decision was comprehended by 13 forms of behavioural theories, with the top-ranked two being the diffusion of innovation and the theory of planned behaviour. The literature showcased a total of 170 quantitative, 20 qualitative, and nine mixed-method studies, with statistical and non-statistical techniques being applied 139 and 86 times, respectively. Regression analysis was the most commonly used statistical analysis method, followed by spatial analysis for non-statistical models. At the heart of the predictive methods for analysing the diffusion rate of these solar technologies, nuances of 25 agent-based models and their social networks were examined in depth. The review further revealed 14 spectra of household categories as well as 12 typologies of household comparisons.

Given the challenges associated with drafting technology-transfer contracts, we examine decisions to involve third parties offering technical or legal support in the contract-drafting process. We first argue that the attributes of the transaction are key drivers of third-party involvement. We then draw on the behavioral theory of the firm to develop arguments regarding the influence of third parties on contract complexity. Our results reveal that the involvement of legal third parties tends to magnify the contract’s overall complexity. In contrast, the involvement of technical third parties reduces the inclusion of monitoring provisions and increases the inclusion of coordination provisions.