A product diffusion model incorporating repeat purchases

Olson, Jerome A.; Choi, Syngjoo

doi:10.1016/0040-1625(85)90019-8

Cited by 70 publications

(28 citation statements)

References 10 publications

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“…These models address, among other things, (1) replacement purchasing and repeat purchasing [9][10][11][12]; (2) supply restriction [13]; (3) and diffusion at the brand level [14]. Diffusion models, however, have some limitations when forecasting the demand for new technology because they usually depend on historical time series data.…”

Section: Literature Reviewmentioning

confidence: 99%

Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Lee

2017

Sustainability

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Abstract:The forecasting demand for new technology for which few historical data observations are available is difficult but essential to sustainable development. The current study suggests an alternative forecasting methodology based on a hazard rate model using stated and revealed preferences of consumers. In estimating the hazard rate, information is initially derived through conjoint analysis based on a consumer survey and then updated using Bayes' theorem with available market data. To compare the proposed models' performance with benchmark models, the Bass model, the logistic growth model, and a Bayesian approach based on analogy are adopted. The results show that the proposed model outperforms the benchmark models in terms of pre-launch and post-launch forecasting performances.

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Section: Literature Reviewmentioning

confidence: 99%

Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Lee

2017

Sustainability

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“…Two of these studies, by Lilien et al (1981) and Mahajan et al (1983), include repeat purchase in the Bass model to examine diffusion of ethical drugs. The other two studies, by Olson and Choi (1985) and Kamakura and Balasubramanian (1987), include product replacements in the Bass model to assess long-term sales for consumer durable products. Norton and Bass (1987) assume that adopters continue to buy and that the average repeat buying rate over the population of adopters is constant.…”

Section: Product and Market Characteristics Do Not Influence Diffusiomentioning

confidence: 99%

Diffusion of Technologies and Social Behavior

1991

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“…Simple though it may be, the Bass model has been used widely to analyze demand and diffusion in such fields as management, policy, economics, and marketing. The Bass model has been improved upon to reflect replacement and repeat purchasing [8][9][10][11], competition between different technologies [12], and diffusion at the brand level [13,14], among others.…”

Section: Introductionmentioning

confidence: 99%

Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases

Lee

Huh

2017

Sustainability

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Understanding the nature of the diffusion process is crucial for sustainable development of a new technology and product. This study introduces a replacement diffusion model that leads to a better understanding of the growth dynamics of a technology. The model operates in an environment with multiple competitors and overcomes the limitations of existing models. The model (1) consists of a diffusion model and an additional time series model; (2) separately identifies the diffusion of first-time purchases and that of replacement purchases; (3) incorporates players' marketing-mix variables, affecting a new technology diffusion; and (4) characterizes consumers' different replacement cycles. The proposed model is applied to South Korea's mobile handset market. The model performs well in terms of its fit and forecasting capability when compared with other diffusion models incorporating replacement and repeat purchases. The usefulness of the model stems from its ability to describe complicated environments and its flexibility in including multiple factors that drives diffusion in the regression analysis.

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A product diffusion model incorporating repeat purchases

Cited by 70 publications

References 10 publications

Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Demand Forecasting in the Early Stage of the Technology’s Life Cycle Using a Bayesian Update

Diffusion of Technologies and Social Behavior

Technology Forecasting Using a Diffusion Model Incorporating Replacement Purchases

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