Browsing by Subject "diffusion of innovations"

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  • Räihä, Jouni; Ruokamo, Enni (Elsevier, 2021)
    Energy and Buildings 251 (2021), 111366
    Detached house owners can improve energy efficiency in heating by adding a supplementary heating system alongside the primary mode. Whereas research on primary heating mode adoption is wide, studies focusing solely on the determinants of supplementary heating system adoption is limited. This study examines the determinants of supplementary heating system adoption and consideration in Finland with a survey data collected from a sample of newly built detached house owners. We employ discrete choice modeling to investigate the homeowners’ supplementary heating system choices and interpret the results vis-à-vis the diffusion of innovations literature. The supplementary heating systems under study are solar panel, solar thermal heater, air-source heat pump and water-circulating fireplace. Overall, the findings indicate that homeowners are generally receptive to supplementary heating in Finland. The analyses show that several factors such as age, education, primary heating mode, heating system attributes, location, environmental attitudes and information channels impact the supplementary heating system adoption decision.
  • de Vocht, Miikka; Laherto, Antti Mikko Petteri (2017)
    In order to facilitate policy‐driven reforms in science education, it is important to understand how teaching innovations diffuse among teachers and how that adoption process can be catalysed. Little is known about the set of attitudes that makes teachers early or late adopters. In this study, the Concerns‐Based Adoption Model (C‐BAM) was employed as a framework for analysing teachers’ interests, concerns, worries and enthusiasm. We argue that the questionnaire typically used with C‐BAM suffers from a ceiling effect and has unbalanced variables. An improved version of the questionnaire was developed and implemented in the project IRRESISTIBLE with 180 science teachers in ten countries at all school levels. The case of educational innovation in this project was Responsible Research and Innovation (RRI), a concept offered by the EU for science education to orient towards socially and ethically sensitive and inclusive processes of science and technology. Using cluster analysis we found four concern profile types: the Carefree, the Pragmatic, the Uncertain and the Worried. With their relatively high positive interests, the Carefree and the Pragmatic profile types are most likely to be early adopters. The high number of Uncertain teachers calls for better conceptualization of RRI in the school context. Furthermore, teacher professional development and additional resources are needed if this innovation is to be diffused widely across European schools. The improved questionnaire provided elaborate information on teachers’ concerns and interests, and could help in understanding and facilitating other top‐down educational reforms as well.
  • Lerviks, Alf-Erik (Svenska handelshögskolan, 2004)
    Research Reports
    A diffusion/replacement model for new consumer durables designed to be used as a long-term forecasting tool is developed. The model simulates new demand as well as replacement demand over time. The model is called DEMSIM and is built upon a counteractive adoption model specifying the basic forces affecting the adoption behaviour of individual consumers. These forces are the promoting forces and the resisting forces. The promoting forces are further divided into internal and external influences. These influences are operationalized within a multi-segmental diffusion model generating the adoption behaviour of the consumers in each segment as an expected value. This diffusion model is combined with a replacement model built upon the same segmental structure as the diffusion model. This model generates, in turn, the expected replacement behaviour in each segment. To be able to use DEMSIM as a forecasting tool in early stages of a diffusion process estimates of the model parameters are needed as soon as possible after product launch. However, traditional statistical techniques are not very helpful in estimating such parameters in early stages of a diffusion process. To enable early parameter calibration an optimization algorithm is developed by which the main parameters of the diffusion model can be estimated on the basis of very few sales observations. The optimization is carried out in iterative simulation runs. Empirical validations using the optimization algorithm reveal that the diffusion model performs well in early long-term sales forecasts, especially as it comes to the timing of future sales peaks.