Is product life cycle theory a special case? Dominant designs and the emergence of market niches through coevolutionary-learning

Windrum, Paul; Birchenhall, Chris

doi:10.1016/s0954-349x(97)00039-8

Cited by 146 publications

(69 citation statements)

References 11 publications

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“…In the construction of our model we found inspiration in a number of growth models, both endogenous (Romer, 1990, Aghion, Howitt, 1998 and evolutionary (Silverberg, Verspagen, 1993;Dosi et al, 1994, Windrum, Birchenhall, 1998 as well as on empirical work on structural change (Fagerberg, Verspagen, 2002). The model also bears some similarity of style to history friendly models (Malerba et al 1999).…”

Section: ) a Model Of Economic Development By The Creation Of New Sementioning

confidence: 98%

Generalized barriers to entry and economic development

Saviotti

Pyka

2010

J Evol Econ

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Section: ) a Model Of Economic Development By The Creation Of New Sementioning

confidence: 98%

Generalized barriers to entry and economic development

Saviotti

Pyka

2010

J Evol Econ

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“…Firms thus learn (directly, i.e. without own R&D) from the use of products of rivals, which is referred to by some as emulative learning (Windrum and Birchenhall 1998). Second, the increasing number of launching competitors (due to rising consumer sales) will drive up the R&D investment levels of all firms, which will increase their technological capabilities.…”

Section: Increasing Returns-to-scale and Learning-by-doingmentioning

confidence: 99%

Incorporating social context and co-evolution in an innovation diffusion model—with an application to cleaner vehicles

Dijk¹,

Kemp²,

Valkering³

2011

J Evol Econ

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Existing diffusion models have proven less suitable for the analysis of environmental innovations, such as hybrid vehicles, which emerge in a context of changing social appraisal and regulatory support. In this paper, we offer an agent-based analysis of innovation diffusion which is better suitable for those cases. We explore future scenarios of car engine technology with support of a simulation model. In the model, actor behavior is modeled explicitly on the basis of actor frames and the consequent appraisal of technology options by potential adopters and by suppliers. The model is analytically novel in that it incorporates five feedback loops: interactive learning between suppliers and users, scale and learning economies, endogenous taste formation among consumers, and social learning (attribution of meaning). In the model, consumer decisions influence each other and the supply of innovation by shaping R&D and investment decisions of suppliers. The model is applied to explore future diffusion scenarios of clean vehicles, though it starts with simulating recent historic trends, including the quick diffusion of direct fuel injection systems and slow diffusion of electric vehicles in the last 20 years. It is shown that the success of hybrid electric vehicles critically depends on suppliers shifting towards HEV development and production (resulting in greater supply of HEV models) and on consumers valuing hybrid electric vehicles more than environmentally improved diesel vehicles.

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“…When competing designs vary in the advantages they generate for different categories of users, each may develop its own installed base with enough critical mass, and the subsequent lock-in effect prevents one from winning (Arthur 1990). Also Windrum and Birchenhall (1998) and Frenken et al (1999) observed industries with various market niches and reported that alternative designs may compete or converge to a single design per niche. Westarp et al (2000) report a case of geographical differences in market acceptance of a design for Electronic Data Interchange: In the USA the (American) ANSI X12 standard remained dominant whereas in Germany the (international) EDIFACT standard became most popular.…”

Section: Technology Analysis and Strategic Managementmentioning

confidence: 99%