IDEAS home Printed from https://ideas.repec.org/p/ris/fcnwpa/2019_015.html
   My bibliography  Save this paper

Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion

Author

Listed:
  • Liu, Xueying

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

  • Madlener, Reinhard

    (E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN))

Abstract

Over the past decades, the aviation sector has seen an unprecedented technology advancement as well as rise in global air traffic. In this paper, we establish a two-stage model, combining an epidemic diffusion model and a regression analysis to analyze and predict the diffusion process of modern commercial aviation aircraft models before they are launched into the market. For the estimation of the first-stage epidemic diffusion model, a total of 19,768 delivery records covering 52 widely used aircraft models are used and a non-linear least squares method is applied. For the second-stage regression analysis, we collect aircraft specific technical parameters including range, maximum take-off weight and emissions. We find that, at present, pollutant emissions are not of key significance in determining the success of market diffusion of aircraft models, while conventional parameters, such as range, takeoff weight, and bypass ratio of the engine, are comparably more significant. In terms of projection into the future, our model is relatively good at predicting the rate of diffusion but less so at predicting the market size. This naturally points to further research avenues in terms of the prediction of market potentials.

Suggested Citation

  • Liu, Xueying & Madlener, Reinhard, 2019. "Get Ready for Take-Off: A Two-Stage Model of Aircraft Market Diffusion," FCN Working Papers 15/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    • Handle: RePEc:ris:fcnwpa:2019_015
    as

    Download full text from publisher

    File URL: https://www.fcn.eonerc.rwth-aachen.de/global/show_document.asp?id=aaaaaaaaajtmlsw
    File Function: Full text
    Download Restriction: no
    ---><---

    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. Francesco Caselli & Wilbur John Coleman, 2001. "Cross-Country Technology Diffusion: The Case of Computers," American Economic Review, American Economic Association, vol. 91(2), pages 328-335, May.
    3. Specht, Jan Martin & Madlener, Reinhard, 2018. "Business Models for Energy Suppliers Aggregating Flexible Distributed Assets and Policy Issues Raised," FCN Working Papers 7/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Bronwyn H. Hall, 2004. "Innovation and Diffusion," NBER Working Papers 10212, National Bureau of Economic Research, Inc.
    5. Höfer, Tim & Madlener, Reinhard, 2018. "Locational (In-)Efficiency of Renewable Power Generation Feeding in the Electricity Grid: A Spatial Regression Analysis," FCN Working Papers 13/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised 01 Dec 2019.
    6. Heesen, Florian & Madlener, Reinhard, 2021. "Revisiting heat energy consumption modeling: Household production theory applied to field experimental data," Energy Policy, Elsevier, vol. 158(C).
    7. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    10. Wolff, Stefanie & Madlener, Reinhard, 2019. "Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning," FCN Working Papers 3/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    11. Atasoy, Ayse Tugba & Harmsen-van Hout, Marjolein & Madlener, Reinhard, 2018. "Strategic Demand Response to Dynamic Pricing: A Lab Experiment for the Electricity Market," FCN Working Papers 5/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Jan 2020.
    12. Karami, Mahdi & Madlener, Reinhard, 2019. "Smart Predictive Maintenance Strategy Based on Cyber-Physical Systems for Centrifugal Pumps: A Bearing Vibration Analysis," FCN Working Papers 14/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    13. Frank M. Bass & Kent Gordon & Teresa L. Ferguson & Mary Lou Githens, 2001. "DIRECTV: Forecasting Diffusion of a New Technology Prior to Product Launch," Interfaces, INFORMS, vol. 31(3_supplem), pages 82-93, June.
    14. Zeng, Yu & Schmitz, Hendrik & Madlener, Reinhard, 2018. "An Econometric Analysis of the Determinants of Passenger Vehicle Sales in Germany," FCN Working Papers 6/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    15. 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.
    16. 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.
    17. Henrich R. Greve & Marc-David L. Seidel, 2015. "The thin red line between success and failure: Path dependence in the diffusion of innovative production technologies," Strategic Management Journal, Wiley Blackwell, vol. 36(4), pages 475-496, April.
    18. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    19. Höfer, Tim & von Nitzsch, Rüdiger & Madlener, Reinhard, 2019. "Using Value-Focused Thinking and Multi-Criteria Group Decision-Making to Evaluate Energy Transition Alternatives," FCN Working Papers 4/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    20. Barbara Glensk & Reinhard Madlener, 2019. "Energiewende @ Risk: On the Continuation of Renewable Power Generation at the End of Public Policy Support," Energies, MDPI, vol. 12(19), pages 1-25, September.
    21. Höfer, Tim & Madlener, Reinhard, 2020. "A participatory stakeholder process for evaluating sustainable energy transition scenarios," Energy Policy, Elsevier, vol. 139(C).
    22. Lee, Chul-Yong & Huh, Sung-Yoon, 2017. "Forecasting the diffusion of renewable electricity considering the impact of policy and oil prices: The case of South Korea," Applied Energy, Elsevier, vol. 197(C), pages 29-39.
    23. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    24. Karami, Mahdi & Madlener, Reinhard, 2018. "Business Model Innovation for the Energy Market: Joint Value Creation for Electricity Retailers and their Residential Customers," FCN Working Papers 15/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    25. Husemann, Michael & Schäfer, Katharina & Stumpf, Eike, 2018. "Flexibility within flight operations as an evaluation criterion for preliminary aircraft design," Journal of Air Transport Management, Elsevier, vol. 71(C), pages 201-214.
    26. 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.
    27. Wolff, Stefanie & Madlener, Reinhard, 2018. "Driven by Change: Commercial Drivers’ Acceptance and Perceived Efficiency of Using Light-Duty Electric Vehicles in Germany," FCN Working Papers 11/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    28. Vonsien, Silvia & Madlener, Reinhard, 2018. "Cost-Effectiveness of Li-Ion Battery Storage with a Special Focus on Photovoltaic Systems in Private Households," FCN Working Papers 2/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    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. Wolff, Stefanie & Madlener, Reinhard, 2020. "Willing to Pay? Spatial Heterogeneity of e-Vehicle Charging Preferences in Germany," FCN Working Papers 9/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    3. Hellwig, Robert & Atasoy, Ayse Tugba & Madlener, Reinhard, 2020. "The Impact of Social Preferences and Information on the Willingness to Pay for Fairtrade Products," FCN Working Papers 6/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Walter, Antonia & Held, Maximilian & Pareschi, Giacomo & Pengg, Hermann & Madlener, Reinhard, 2020. "Decarbonizing the European Automobile Fleet: Impacts of 1.5 °C-compliant Climate Policies in Germany and Norway," FCN Working Papers 18/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).

    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. Liu, Xueying & Madlener, Reinhard, 2021. "The sky is the limit: Assessing aircraft market diffusion with agent-based modeling," Journal of Air Transport Management, Elsevier, vol. 96(C).
    2. Wolff, Stefanie & Madlener, Reinhard, 2019. "Charged up? Preferences for Electric Vehicle Charging and Implications for Charging Infrastructure Planning," FCN Working Papers 3/2019, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    3. Wolff, Stefanie & Madlener, Reinhard, 2020. "Willing to Pay? Spatial Heterogeneity of e-Vehicle Charging Preferences in Germany," FCN Working Papers 9/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    4. Hellwig, Robert & Atasoy, Ayse Tugba & Madlener, Reinhard, 2020. "The Impact of Social Preferences and Information on the Willingness to Pay for Fairtrade Products," FCN Working Papers 6/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    5. Walter, Antonia & Held, Maximilian & Pareschi, Giacomo & Pengg, Hermann & Madlener, Reinhard, 2020. "Decarbonizing the European Automobile Fleet: Impacts of 1.5 °C-compliant Climate Policies in Germany and Norway," FCN Working Papers 18/2020, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    10. Brito, Thiago Luis Felipe & Islam, Towhidul & Stettler, Marc & Mouette, Dominique & Meade, Nigel & Moutinho dos Santos, Edmilson, 2019. "Transitions between technological generations of alternative fuel vehicles in Brazil," Energy Policy, Elsevier, vol. 134(C).
    11. Kivi, Antero & Smura, Timo & Töyli, Juuso, 2012. "Technology product evolution and the diffusion of new product features," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 107-126.
    12. Kurdgelashvili, Lado & Shih, Cheng-Hao & Yang, Fan & Garg, Mehul, 2019. "An empirical analysis of county-level residential PV adoption in California," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 321-333.
    13. Jha, Ashutosh & Saha, Debashis, 2020. "“Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models”," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    14. Jinah Yang & Daiki Min & Jeenyoung Kim, 2020. "The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    15. Franses, Philip Hans, 2021. "Modeling box office revenues of motion pictures✰," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    16. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2019. "Modeling Technological Substitution by Incorporating Dynamic Adoption Rate," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(01), pages 1-24, February.
    17. Specht, Jan Martin & Madlener, Reinhard, 2018. "Business Models for Energy Suppliers Aggregating Flexible Distributed Assets and Policy Issues Raised," FCN Working Papers 7/2018, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    18. Lee, Hakyeon & Kim, Sang Gook & Park, Hyun-woo & Kang, Pilsung, 2014. "Pre-launch new product demand forecasting using the Bass model: A statistical and machine learning-based approach," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 49-64.
    19. Byrka, Katarzyna & Jȩdrzejewski, Arkadiusz & Sznajd-Weron, Katarzyna & Weron, Rafał, 2016. "Difficulty is critical: The importance of social factors in modeling diffusion of green products and practices," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 723-735.
    20. Mukisa, Nicholas & Zamora, Ramon & Lie, Tek Tjing, 2021. "Diffusion forecast for grid-tied rooftop solar photovoltaic technology under store-on grid scheme model in Sub-Saharan Africa: Government role assessment," Renewable Energy, Elsevier, vol. 180(C), pages 516-535.

    More about this item

    Keywords

    Transportation economics; Technological diffusion; Epidemic Diffusion Model; Aircraft;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q53 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Air Pollution; Water Pollution; Noise; Hazardous Waste; Solid Waste; Recycling
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ris:fcnwpa:2019_015. 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: Hendrik Schmitz (email available below). General contact details of provider: https://edirc.repec.org/data/fceonde.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.