Demographic processes in a model of innovation diffusion with dynamic market

Centrone, Francesca; Goia, Aldo; Salinelli, Ernesto

doi:10.1016/j.techfore.2006.02.006

Cited by 37 publications

(12 citation statements)

References 30 publications

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“…However, despite the model's simplicity, some basic assumptions of the Bass model are not consistent with observed reality: in a real market, the market potential is in dynamic variation, marketing strategies exist, new products are upgraded, and different products can be influenced by one another's sales performance. Therefore, some efforts have been made to modify the Bass model (Centrone, Goia, & Salinelli, ; Norton & Bass, ; Bagchi, Kirs, & López, 2008; Islam, Fiebig, & Meade, ; Roberts, Nelson, & Morrison, ). Nonetheless, research (Chandrasekaran & Tellis, ) shows that the Bass model fits actual data almost as well as much more complex models which seek to correct its limitations (Bass, Krishnan, & Jain, ).…”

Section: Related Workmentioning

confidence: 99%

Innovation or imitation: The diffusion of citations

Min

Ding

et al. 2018

Asso for Info Science & Tech

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Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.

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Section: Related Workmentioning

confidence: 99%

Innovation or imitation: The diffusion of citations

Min

Ding

et al. 2018

Asso for Info Science & Tech

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“…Not Behavioural approaches are predominantly based on system dynamics, diffusion and time series models (a recent and interesting review may be found in Al-Alawi and Bradley, 2013). Most notable contributions are mainly founded on the Bass diffusion model (1969) (McManus and Senter, 2010Centrone et al, 2007;Cao, 2006;Jeon, 2010;Becker et al, 2009;Won et al, 2009;Higgins et al, 2012), the Gompertz growth model (Muraleedharakurup et al, 2010;Lamberson, 2009) or the logistic model (Trappey and Wu, 2008;Muraleedharakurup et al, 2010).…”

Section: Technological Frameworkmentioning

confidence: 99%

Modelling the adoption intention and installation choice of an automotive after-market mild-solar-hybridization kit

Luca

Pace

Marano

2015

Transportation Research Part C: Emerging Technologies

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“…This technique is applied to fields such as population dynamics, consumer behavior and vehicle traffic. Consumer choice models have been used in numerous studies (Islam and Meade, 1997;Cao, 2004;Mau, 2005;Centrone et al, 2007;Struben and Sterman, 2008;Sullivan et al, 2009;Becker, 2009;Muraleedharakurup et al, 2010;Eppstein et al, 2011;Zhang et al, 2011;McManus and Senter, 2010) to estimate vehicle sales and are usually derived from past vehicle sales data and consumer demographic data. The goal of diffusion rate and time series models is to find the "life cycle of new products over time".…”

Section: Hybrid Electric Vehiclesmentioning

confidence: 99%