IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v107y2016icp130-140.html
   My bibliography  Save this article

The effect of service rollout on demand forecasting: The application of modified Bass model to the step growing markets

Author

Listed:
  • Velickovic, Stevan
  • Radojicic, Valentina
  • Bakmaz, Bojan

Abstract

The purpose of this research is to develop a new forecasting model for gradual development of telecommunications network infrastructure. If technical support service is not provided in the whole territory, then the service cannot be simultaneously offered to the market. Therefore, the adoption process is performing in steps, following the systematic process of the infrastructure development. We propose the step growing market model with variable market potential, based on the fundamental Bass diffusion model. This model uses market segmentation and the simulation of various market scenarios in order to indicate effective service rollout. Demand forecasting is required in order to dimensioning of required network resources. Moreover, this model offers the insight into the operator's compromises regarding decisions in the network investment. The resulting plan is a network deployment strategy that defines temporal sequence of network layouts. Thus, the upgrade plan indicates which investments are indispensible at each point of time.

Suggested Citation

  • Velickovic, Stevan & Radojicic, Valentina & Bakmaz, Bojan, 2016. "The effect of service rollout on demand forecasting: The application of modified Bass model to the step growing markets," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 130-140.
  • Handle: RePEc:eee:tefoso:v:107:y:2016:i:c:p:130-140
    DOI: 10.1016/j.techfore.2016.03.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162516000846
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.techfore.2016.03.018?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. John A. Norton & Frank M. Bass, 1987. "A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High-Technology Products," Management Science, INFORMS, vol. 33(9), pages 1069-1086, September.
    2. Carey, John & Moss, Mitchell L., 1985. "The diffusion of new telecommunication technologies," Telecommunications Policy, Elsevier, vol. 9(2), pages 145-158, June.
    3. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    4. Jain, Dipak C & Rao, Ram C, 1990. "Effect of Price on the Demand for Durables: Modeling, Estimation, and Findings," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 163-170, April.
    5. Vijay Mahajan & Robert A. Peterson, 1978. "Innovation Diffusion in a Dynamic Potential Adopter Population," Management Science, INFORMS, vol. 24(15), pages 1589-1597, November.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Dan Horsky, 1990. "A Diffusion Model Incorporating Product Benefits, Price, Income and Information," Marketing Science, INFORMS, vol. 9(4), pages 342-365.
    8. Hubert Gatignon & Jehoshua Eliashberg & Thomas S. Robertson, 1989. "Modeling Multinational Diffusion Patterns: An Efficient Methodology," Marketing Science, INFORMS, vol. 8(3), pages 231-247.
    9. 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.
    10. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    11. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    12. Rui Baptista, 1999. "The Diffusion of Process Innovations: A Selective Review," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 6(1), pages 107-129.
    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. Edward Oughton, 2018. "Towards 5G: scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure," Working Papers 2017/04 (revised), Cambridge Judge Business School, University of Cambridge.
    2. Szalkowski, Gabriel Andy & Mikalef, Patrick, 2023. "Understanding digital platform evolution using compartmental models," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    3. Edward Oughton, 2017. "Towards 5G: scenario-based assessment of the future supply and demand for mobile telecommunications infrastructure," Working Papers 2017/04, Cambridge Judge Business School, University of Cambridge.
    4. 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).
    5. Pannhorst, Matthias & Dost, Florian, 2019. "Marketing innovations to old-age consumers: A dynamic Bass model for different life stages," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 315-327.
    6. Han, Zhongya & Tang, Zhongjun & He, Bo, 2022. "Improved Bass model for predicting the popularity of product information posted on microblogs," Technological Forecasting and Social Change, Elsevier, vol. 176(C).

    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. Peters, Kay & Albers, Sönke & Kumar, V., 2008. "Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables," EconStor Preprints 27678, ZBW - Leibniz Information Centre for Economics.
    3. Wei-yu Kevin Chiang, 2012. "Supply Chain Dynamics and Channel Efficiency in Durable Product Pricing and Distribution," Manufacturing & Service Operations Management, INFORMS, vol. 14(2), pages 327-343, April.
    4. 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).
    5. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    6. Dragan Lazarević & Libor Švadlenka & Valentina Radojičić & Momčilo Dobrodolac, 2020. "New Express Delivery Service and Its Impact on CO 2 Emissions," Sustainability, MDPI, vol. 12(2), pages 1-29, January.
    7. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    8. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2020. "Modeling technology diffusion: a study based on market coverage and advertising efforts," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 154-162, July.
    9. Kim, Namwoon & Srivastava, Rajendra K., 2007. "Modeling cross-price effects on inter-category dynamics: The case of three computing platforms," Omega, Elsevier, vol. 35(3), pages 290-301, June.
    10. Hongmin Li, 2020. "Optimal Pricing Under Diffusion-Choice Models," Operations Research, INFORMS, vol. 68(1), pages 115-133, January.
    11. Han, Zhongya & Tang, Zhongjun & He, Bo, 2022. "Improved Bass model for predicting the popularity of product information posted on microblogs," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    12. Namwoon Kim & Dae Ryun Chang & Allan D. Shocker, 2000. "Modeling Intercategory and Generational Dynamics for A Growing Information Technology Industry," Management Science, INFORMS, vol. 46(4), pages 496-512, April.
    13. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2021. "Technology diffusion model with change in adoption rate and repeat purchases: a case of consumer balking," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(1), pages 29-36, February.
    14. Shi, Xiaohui & Li, Feng & Bigdeli, Ali Ziaee, 2016. "An examination of NPD models in the context of business models," Journal of Business Research, Elsevier, vol. 69(7), pages 2541-2550.
    15. Hongmin Li & Dieter Armbruster & Karl G. Kempf, 2013. "A Population-Growth Model for Multiple Generations of Technology Products," Manufacturing & Service Operations Management, INFORMS, vol. 15(3), pages 343-360, July.
    16. Ferreira, Kevin D. & Lee, Chi-Guhn, 2014. "An integrated two-stage diffusion of innovation model with market segmented learning," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 189-201.
    17. Adarsh Anand & Richie Aggarwal & Ompal Singh, 2019. "Using Weibull Distribution for Modeling Bimodal Diffusion Curves: A Naive Framework to Study Product Life Cycle," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(07), pages 1-17, November.
    18. Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
    19. Bing Jing, 2011. "Social Learning and Dynamic Pricing of Durable Goods," Marketing Science, INFORMS, vol. 30(5), pages 851-865, September.
    20. Samuel Sale, R. & Mesak, Hani I. & Inman, R. Anthony, 2017. "A dynamic marketing-operations interface model of new product updates," European Journal of Operational Research, Elsevier, vol. 257(1), pages 233-242.

    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:eee:tefoso:v:107:y:2016:i:c:p:130-140. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.