Simulating and Forecasting the Demand for New Consumer Durables

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dc.contributor Svenska handelshögskolan, Institutionen för marknadsföring och företagsgeografi, marknadsföring sv
dc.contributor Swedish School of Economics and Business Administration, Department of Marketing and Corporate Geography, Marketing en
dc.contributor.author Lerviks, Alf-Erik
dc.date.accessioned 2011-03-02T14:12:51Z
dc.date.available 2007-09-28T12:08:49Z fi
dc.date.available 2011-03-02T14:12:51Z
dc.date.issued 2004-05-28
dc.identifier.isbn 951-555-825-5
dc.identifier.issn 0357-5764
dc.identifier.uri http://hdl.handle.net/10227/239
dc.identifier.uri URN:ISBN:951-555-825-5
dc.description.abstract 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. fi
dc.language.iso en
dc.publisher Svenska handelshögskolan sv
dc.publisher Swedish School of Economics and Business Administration en
dc.relation.ispartofseries Research Reports
dc.relation.ispartofseries 59
dc.rights Publikationen är skyddad av upphovsrätten. Den får läsas och skrivas ut för personligt bruk. Användning i kommersiellt syfte är förbjuden. sv
dc.rights This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. en
dc.rights Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty. fi
dc.subject consumer durables fi
dc.subject diffusion of innovations fi
dc.subject adoption behaviour fi
dc.subject interpersonal communication fi
dc.subject word of mouth fi
dc.subject diffusion models fi
dc.subject growth curve models fi
dc.subject simulation models fi
dc.subject forecasting fi
dc.subject calibration fi
dc.subject replacement demand fi
dc.subject.other Marketing fi
dc.title Simulating and Forecasting the Demand for New Consumer Durables fi
dc.type.dcmitype Text

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