Purpose: The conceptualisation of technology adoption has largely been based on the Bass or some Bass-derived model - notably, the logistic model. Logistic-type models offer limited insights regarding the adoption process dynamics or the utility value of their results. The purpose of this paper is to outline an alternative technology adoption framework based on complex adaptive networks. Design/methodology/approach: An agent-based methodological approach is proposed. In it the actors, factors, goals, and adaptive learning influences driving solar energy technology adoption (SETA) process are first substantiated by empirical evidence gathered using field questionnaires and then incorporated in the simulation of a dynamic complex adaptive network of SETA. The complex adaptive network model is based on simple heuristic rules applied using a modified preferential attachment scheme within a NetLogo simulation environment. Findings: The interim results suggest an emergent network where prominent hub "driver" agents underlining the robustness of the model are statistically discernible. Research limitations/implications: The research is limited to solar photovoltaic and solar water heating technology adoption in Botswana households; however, its results are far-reaching. Practical implications: These results can be related to sustainable energy policy design. There, targeted incentive mechanisms can be formulated against the backdrop of the identified environmental factors and actors; the aim being to accelerate and cascade SETA. Social implications: The results could also be cascaded to other sectors and other non-solar technologies, thus providing a general alternative framework for enabling the widespread adoption of technologies. Originality/value: This research therefore represents a novel way of utilizing the new science of networks to accelerate SETA.
All Science Journal Classification (ASJC) codes
- Strategy and Management