[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v31y2012i2p351-364.html
   My bibliography  Save this article

Modeling Seasonality in New Product Diffusion

Author

Listed:
  • Yuri Peers

    (Waikato Management School, University of Waikato, Hamilton 3240, New Zealand)

  • Dennis Fok

    (Econometric Institute, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands)

  • Philip Hans Franses

    (Econometric Institute, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands)

Abstract
We propose a method to include seasonality in any diffusion model that has a closed-form solution. The resulting diffusion model captures seasonality in a way that naturally matches the original diffusion model's pattern. The method assumes that additional sales at seasonal peaks are drawn from previous or future periods. This implies that the seasonal pattern does not influence the underlying diffusion pattern. The model is compared with alternative approaches through simulations and empirical examples. As alternatives, we consider the standard Generalized Bass Model (GBM) and the basic Bass Model, which ignores seasonality. One of the main findings is that modeling seasonality in a GBM generates good predictions but gives biased estimates. In particular, the market potential parameter is underestimated. Ignoring seasonality in cases where data of the entire diffusion period are available gives unbiased parameter estimates in most relevant scenarios. However, ignoring seasonality leads to biased parameter estimates and predictions when only part of the diffusion period is available. We demonstrate that our model gives correct estimates and predictions even if the full diffusion process is not yet available.

Suggested Citation

  • Yuri Peers & Dennis Fok & Philip Hans Franses, 2012. "Modeling Seasonality in New Product Diffusion," Marketing Science, INFORMS, vol. 31(2), pages 351-364, March.
    • Handle: RePEc:inm:ormksc:v:31:y:2012:i:2:p:351-364
    DOI: 10.1287/mksc.1110.0696
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.1110.0696
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.1110.0696?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rajkumar Venkatesan & Trichy V. Krishnan & V. Kumar, 2004. "Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Nonlinear Least Squares," Marketing Science, INFORMS, vol. 23(3), pages 451-464, August.
    2. Bewley, Ronald & Fiebig, Denzil G., 1988. "A flexible logistic growth model with applications in telecommunications," International Journal of Forecasting, Elsevier, vol. 4(2), pages 177-192.
    3. repec:dau:papers:123456789/3445 is not listed on IDEAS
    4. Fok, Dennis & Hans Franses, Philip & Paap, Richard, 2007. "Seasonality and non-linear price effects in scanner-data-based market-response models," Journal of Econometrics, Elsevier, vol. 138(1), pages 231-251, May.
    5. Boswijk, H. Peter & Franses, Philip Hans, 2005. "On the Econometrics of the Bass Diffusion Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 255-268, July.
    6. Christophe Van den Bulte & Gary L. Lilien, 1997. "Bias and Systematic Change in the Parameter Estimates of Macro-Level Diffusion Models," Marketing Science, INFORMS, vol. 16(4), pages 338-353.
    7. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    8. Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
    9. Liran Einav, 2007. "Seasonality in the U.S. motion picture industry," RAND Journal of Economics, RAND Corporation, vol. 38(1), pages 127-145, March.
    10. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    11. Dixon, Robert J, 1980. "Hybrid Corn Revisited," Econometrica, Econometric Society, vol. 48(6), pages 1451-1461, September.
    12. Bernard Pras & Gilles Laurent & Gary L. Lilien, 1994. "Research Traditions in Marketing," Post-Print halshs-00150675, HAL.
    13. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Øverby, Harald & Audestad, Jan A. & Szalkowski, Gabriel Andy, 2023. "Compartmental market models in the digital economy—extension of the Bass model to complex economic systems," Telecommunications Policy, Elsevier, vol. 47(1).
    2. Collins, Matthew & Curtis, John, 2017. "Advertising and investment spillovers in the diffusion of residential energy efficiency renovations," Papers WP569, Economic and Social Research Institute (ESRI).
    3. Fernández-Durán, J.J., 2014. "Modeling seasonal effects in the Bass Forecasting Diffusion Model," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 251-264.
    4. Guidolin, Mariangela & Guseo, Renato, 2014. "Modelling seasonality in innovation diffusion," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 33-40.
    5. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    6. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    2. Christophe Van den Bulte & Yogesh V. Joshi, 2007. "New Product Diffusion with Influentials and Imitators," Marketing Science, INFORMS, vol. 26(3), pages 400-421, 05-06.
    3. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    4. Guseo, Renato & Mortarino, Cinzia & Darda, Md Abud, 2015. "Homogeneous and heterogeneous diffusion models: Algerian natural gas production," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 366-378.
    5. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    6. Fernández-Durán, J.J., 2014. "Modeling seasonal effects in the Bass Forecasting Diffusion Model," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 251-264.
    7. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    8. Krishnan, Trichy V. & Feng, Shanfei & Jain, Dipak C., 2023. "Peak sales time prediction in new product sales: Can a product manager rely on it?," Journal of Business Research, Elsevier, vol. 165(C).
    9. Franses, Ph.H.B.F., 2009. "Forecasting Sales," Econometric Institute Research Papers EI 2009-29, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    10. Singhal, Shakshi & Anand, Adarsh & Singh, Ompal, 2020. "Studying dynamic market size-based adoption modeling & product diffusion under stochastic environment," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    11. Christos Michalakelis & Georgia Dede & Dimitris Varoutas & Thomas Sphicopoulos, 2010. "Estimating diffusion and price elasticity with application to telecommunications," Netnomics, Springer, vol. 11(3), pages 221-242, October.
    12. Guidolin, Mariangela & Guseo, Renato, 2014. "Modelling seasonality in innovation diffusion," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 33-40.
    13. Rajkumar Venkatesan & Trichy V. Krishnan & V. Kumar, 2004. "Evolutionary Estimation of Macro-Level Diffusion Models Using Genetic Algorithms: An Alternative to Nonlinear Least Squares," Marketing Science, INFORMS, vol. 23(3), pages 451-464, August.
    14. Albert C. Bemmaor & Janghyuk Lee, 2002. "The Impact of Heterogeneity and Ill-Conditioning on Diffusion Model Parameter Estimates," Marketing Science, INFORMS, vol. 21(2), pages 209-220, November.
    15. Furlan, Claudia & Guidolin, Mariangela & Guseo, Renato, 2016. "Has the Fukushima accident influenced short-term consumption in the evolution of nuclear energy? An analysis of the world and seven leading countries," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 37-49.
    16. Al-Alawi, Baha M. & Bradley, Thomas H., 2013. "Review of hybrid, plug-in hybrid, and electric vehicle market modeling Studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 190-203.
    17. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    18. Dong, Changgui & Sigrin, Benjamin & Brinkman, Gregory, 2017. "Forecasting residential solar photovoltaic deployment in California," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 251-265.
    19. Guseo, Renato & Schuster, Reinhard, 2021. "Modelling dynamic market potential: Identifying hidden automata networks in the diffusion of pharmaceutical drugs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    20. Jonathan Beck, 2007. "The sales effect of word of mouth: a model for creative goods and estimates for novels," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 31(1), pages 5-23, March.

    More about this item

    Keywords

    diffusion models; seasonality; forecasting;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:31:y:2012:i:2:p:351-364. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.