Evidence-based narratives in European research programming

The article introduces and exemplifies the approach of evidence-based narratives (EBN). The methodology is a product of co-design between policy-making and science, generating robust intelligence for evidence-based policy-making in the Directorate General for Research and Innovation of the European Commission (DG RTD) under the condition of high uncertainty and fragmented evidence. The EBN transdisciplinary approach tackles practical problems of future-oriented policy-making, in this case in the area of programming for research and innovation addressing the Grand Societal Challenge related to climate change and natural resources. Between 2013 and 2018, the EU-funded RECREATE project developed 20 EBNs in a co-development process between scientists and policy-makers. All EBNs are supported with evidence about the underlying innovation system applying the technological innovation systems (TIS) framework. Each TIS analysis features the innovation, its current state of market diffusion and a description of the innovation investment case. Indicators include potential future market sizes, effects on employment and environmental and social benefits. Based on the innovation and TIS function analyses, the EBNs offer policy recommendations. The article ends with a critical discussion of the EBN approach.

BASS Model Analysis in “Crossing the Chasm” in E-Cars Innovation Diffusion Scenarios

In the face of contemporary ecological problems, including the influence of transport and mobility on the greenhouse effect, policy makers see a solution in the technology of electric vehicles (EVs). This innovation has appeared in the automotive market around the world. In this article, Rogers’ innovation diffusion concept was used to determine if and when EVs could replace combustion engine vehicles. The aim of the article is to analyze the current situation of innovative electric vehicle (EV) technology on the European Union (EU) market and the potential and possible directions of its market diffusion. As a result of the analysis of the current situation of the EU automotive markets, three groups of countries with different market conditions regarding the development of EV technology were distinguished. The classification was carried out using the K-means clustering algorithm, based on the current level of innovation and the level of development of the LPG-(Liquefied Petroleum Gas) powered car market. Then, a group of countries with the greatest advancement in EV technology was selected for further research and market diffusion scenarios were created for this innovation using the Bass model, assuming the target market size of 70–100% of all passenger cars.

Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms

Market diffusion of new products is driven by the actions and reactions of consumers, distributors, competitors, and other stakeholders, all of whom can be heterogeneous in their individual characteristics, attitudes, needs, and objectives. These actors may also interact with others in various ways (e.g., through word of mouth or social influence). Thus, a typical consumer market constitutes a complex system whose behavior is difficult to foresee because stochastic impulses may give rise to complex emergent patterns of system reactions over time. Agent-based modeling, a relatively novel approach to understanding complex systems, is well equipped to deal with this complexity and, therefore, may serve as a valuable tool for both researchers studying particular market effects and practitioners seeking decision support for determining features of products under development or the appropriate combination of measures to accelerate product diffusion in a market. This paper provides an overview of the strengths and criticisms of such tools. It aims to encourage researchers in the field of innovation management, as well as practitioners, to consider agent-based modeling and simulation as a method for gaining deeper insights into market behavior and making better-informed decisions.

Consumer housing values and prejudices against living in wooden homes in the Nordic region

So far, consumer housing values have not been addressed as factors affecting the market diffusion potential of multi-storey wood building (MSWB). To fill the void, this study addresses different types of consumer housing values in Denmark, Finland, Norway, and Sweden (i.e., Nordic region), and whether they affect the likelihood of prejudices against building with wood in the housing markets. The data collected in 2018 from 2191 consumers in the Nordic region were analyzed with exploratory factor analysis and logistic binary regression analysis. According to the results, consumers’ perceptions on ecological sustainability, material usage and urban lifestyle were similar in all countries, while country-specific differences were detected for perceptions on aesthetics and natural milieus. In all countries, appreciating urban lifestyle and living in attractive neighborhoods with good reputation increased the likelihood of prejudices against wood building, while appreciation of aesthetics and natural milieus decreased the likelihood of prejudices. In strengthening the demand for MSWB and sustainable urbanization through actions in businesses (e.g., branding) and via public policy support (e.g., land zoning), few messages derive from the results. In all, abreast with the already existing knowledge on the supply side factors (e.g., wood building innovations), more customized information is needed on the consumer-driven issues affecting the demand potential of MSWB in the housing markets. This would enable, e.g., both enhancing the supply of wooden homes for consumers appreciating urban lifestyle and neighborhoods and fortifying positive image of wood among consumers especially appreciating good architecture and pleasant environmental milieus.

Electric Vehicle Market Diffusion in Main Non–European Markets

Electric vehicles (i.e., battery and plug-in hybrid electric vehicles) are seen as one promising technology toward a sustainable transport system as they have the potential to reduce CO2 emissions. The forecast of their market penetration depends on various factors including the cost development of key components such as the electric battery. This chapter focuses on the impact of experience curves on the battery costs, and consequently on the electric vehicles’ market penetration, which is simulated by coupling two system dynamics transport models: ASTRA, representing Europe, and TE3, representing key non-European car markets. The results of the TE3 model show that the consideration of global endogenous learning curves has an impact on the battery costs and therefore, the development of the electric vehicle stock (“feedback loop”).