Forecasting future demand for large-screen television sets using conjoint analysis with diffusion model

Lee, Jongsu; Cho, Youngsang; Lee, Jeong Dong; Lee, Chul Yong

doi:10.1016/j.techfore.2004.12.002

Cited by 81 publications

(44 citation statements)

References 18 publications

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“…In contrast, choice models do link consumer preferences to product attributes but they usually assume that the preferences remain unchanged (Greene, 2009). A number of studies have attempted to bridge this gap by combining diffusion and choice models to forecast new product demand (Jun and Park, 1999;Kumar and Krishnan, 2002;Jun et al, 2002;Lee et al, 2006;Lee et al, 2008;Eager and Eager, 2011). Jun and Park (1999) combined a diffusion model and a multi-nominal logit (MNL) choice model to forecast multi-generation sales/demand of DRAM (dynamic random access memory).…”

Section: Incorporating Changing Consumer Preferences In Forecasting Mmentioning

confidence: 99%

“…However, for a given generation of a product, the preferences relating to the specified attributes are assumed to remain unchanged during the lifetime of that generation, so that changes in preferences for specific attributes not related to advertising or word of mouth, are not accounted for in these models. Lee et al (2006) also combined choice and diffusion models for forecasting the adoption of flat screen TVs in Korea. The diffusion model was used to forecast the size of the market for all TVs at given time t. The choice modelling was involved in the derivation of what they referred to as a dynamic random utility function for specific products.…”

Section: Incorporating Changing Consumer Preferences In Forecasting Mmentioning

confidence: 99%

“…Despite this there is a lack of research on the extent to which lability can impair accuracy when, as is usually the case, the coefficients estimated in CBC models are assumed to be constant over time. Some researchers have referred to dynamic random utility models (Lee et al, 2006) but the dynamic element of the model is designed to reflect changes in a product's attribute levels (e.g. an increase in its price) rather than changes in the preferences that consumers may have for the attributes even if they remain constant.…”

mentioning

confidence: 99%

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When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

Meeran

Jahanbin

Goodwin

et al. 2017

European Journal of Operational Research

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Forecasting the sales or market share of new products is a major challenge as there is little or no sales history with which to estimate levels and trends. Choice-based conjoint (CBC) is one of the most common approaches used to forecast new products' sales. However, the accuracy of forecasts based on CBC models may be reduced when consumers' preferences for the attributes of products are labile. Despite this, there is a lack of research on the extent to which lability can impair accuracy when the coefficients estimated in CBC models are assumed to be constant over time. This paper aims to address this research gap by investigating the prevalence of lability for consumer durable products and its potential impact on the accuracy of forecasts. There are reasons to expect that lability may be particularly evident where a product is subject to rapid technological change and has a short product life-cycle. We carried out a longitudinal survey of the preferences of 161 potential consumers relating to four different types of products. We established that for both functional and innovative products: (i) the CBC models vary significantly over time, indicating changes in consumer preferences and (ii) such changes may cause large differences in forecasts of the probabilities that consumers will purchase particular brands of products. Hence employing models where coefficients do not change over time can potentially lead to inaccurate market share forecasts for high-tech, short life-cycle products that are launched even a short time after the choice-based modelling has been conducted.Key words: Forecasting, New product forecasting, Choice based conjoint, Attribute coefficient change, Longitudinal study 2 When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable? ABSTRACTForecasting the sales or market share of new products is a major challenge as there is little or no sales history with which to estimate levels and trends. Choice-based conjoint (CBC) is one of the most common approaches used to forecast new products' sales. However, the accuracy of forecasts based on CBC models may be reduced when consumers' preferences for the attributes of products are labile. Despite this, there is a lack of research on the extent to which lability can impair accuracy when the coefficients estimated in CBC models are assumed to be constant over time. This paper aims to address this research gap by investigating the prevalence of lability for consumer durable products and its potential impact on the accuracy of forecasts. There are reasons to expect that lability may be particularly evident where a product is subject to rapid technological change and has a short product life-cycle. We carried out a longitudinal survey of the preferences of 161 potential consumers relating to four different types of products. We established that for both functional and innovative products: (i) the CBC models vary significantly over time, indicating changes in consumer preferences and (ii) such changes may ca...

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Section: Incorporating Changing Consumer Preferences In Forecasting Mmentioning

confidence: 99%

Section: Incorporating Changing Consumer Preferences In Forecasting Mmentioning

confidence: 99%

mentioning

confidence: 99%

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When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

Meeran

Jahanbin

Goodwin

et al. 2017

European Journal of Operational Research

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“…3. The nature of an innovation does not change over time Although Mahajan et al and others (for example Lee et al 2006) mention studies that overcome several assumptions, the models remain one sided in explaining the driving forces of innovation diffusion processes (still spread of information). Other relevant feedback effects, such as progressive improvement of the product (affecting adopter attitudes), remain neglected.…”

Section: Understanding Innovation Diffusion: From Epidemic Modelling mentioning

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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“…Por exemplo, Kim, Lee e Kim (2005) propõem um modelo de escolha discreta adaptado, incorporando o comportamento de adesão do consumidor às dinâmicas de difusão do produto. Lee et al (2006) apresentam uma abordagem similar, integrando o modelo de escolha discreta e o modelo de Bass (1969).…”

Section: Introductionunclassified

Um novo modelo de previsão de demanda para inovações radicais

Neumann¹,

Santa-Eulalia

Yoshino

et al. 2013

Prod.

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ResumoEste trabalho apresenta um novo modelo para a previsão de demanda de inovações radicais baseada em simulação de Dinâmica de Sistemas que combina conceitos do modelo de difusão de Bass e do modelo de escolha discreta. Diferentemente de outras abordagens existentes, esta proposta permite estimar não somente a fatia de mercado do produto, mas também seu comportamento no tempo (timing), a partir das preferências individuais do consumidor e das forças que as influenciam. O modelo proposto pode ser facilmente parametrizado através da Conjoint Analysis e foi testado em escala real no mercado alemão de carros elétricos. Os resultados obtidos colocam em evidência o potencial da abordagem proposta, auxiliando na compreensão dos principais fatores na escolha desse produto.

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Forecasting future demand for large-screen television sets using conjoint analysis with diffusion model

Cited by 81 publications

References 18 publications

When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?

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

Um novo modelo de previsão de demanda para inovações radicais

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