IDEAS home Printed from https://ideas.repec.org/a/eco/journ3/2022-06-1.html
   My bibliography  Save this article

Detecting Chasms and Cracks Using Innovator Scores and Agent Interactions

Author

Listed:
  • Ryo Iwata

    (Department of Industrial and Systems Engineering, Aoyama Gakuin University, Kanagawa, Japan.)

  • Kaoru Kuramoto

    (Department of Industrial and Systems Engineering, Aoyama Gakuin University, Kanagawa, Japan.)

  • Satoshi Kumagai

    (Department of Industrial and Systems Engineering, Aoyama Gakuin University, Kanagawa, Japan.)

Abstract

In chasm theory, it is found from field data that many new products have an initial sales peak followed by a decline. In some cases, this decline lasts for a long period of time, which is named a chasm or crack. In this study, we model the phenomenon using innovator scores and agent-based modelling to understand the factors that cause it. We then conduct a sensitivity analysis of the exogenous variables that determine the behavior of the model. Specifically, we use innovator scores to classify users into innovator theory groups, and build an agent-based model. This study evaluates how cluster connectivity, which represents the word-of-mouth effect between each group, and product recognition range, which represents the advertising effect, affect the chasm or crack phenomenon and new product diffusion. Four scenarios are analyzed with different cluster connectivity and product recognition ranges. Additionally, for each scenario, we perform simulations that consider the interactions between agents and add considerations for new product diffusion measures. Evaluating this model using the behavioral and questionnaire data collected from users of an Online-to-Offline site, it is found that the parameters related to communication in the clusters are factors that cause the occurrence of chasms and cracks.

Suggested Citation

  • Ryo Iwata & Kaoru Kuramoto & Satoshi Kumagai, 2022. "Detecting Chasms and Cracks Using Innovator Scores and Agent Interactions," International Review of Management and Marketing, Econjournals, vol. 12(6), pages 1-15, November.
  • Handle: RePEc:eco:journ3:2022-06-1
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/irmm/article/download/13605/6968
    Download Restriction: no

    File URL: https://www.econjournals.com/index.php/irmm/article/view/13605
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yogendra Kumar & Runa Sarkar & Sanjeev Swami, 2009. "Cluster‐based diffusion: aggregate and disaggregate level modeling," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 6(1), pages 8-26, April.
    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. Christophe Van den Bulte & Stefan Stremersch, 2004. "Social Contagion and Income Heterogeneity in New Product Diffusion: A Meta-Analytic Test," Marketing Science, INFORMS, vol. 23(4), pages 530-544, July.
    4. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    5. Hüseyin İkizler, 2019. "Contagion of network products in small-world networks," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 789-809, December.
    6. Ingeborg Rossow, 2005. "Trends in Wine Consumption in Norway: Is Diffusion Theory Applicable?," Advances in Health Economics and Health Services Research, in: Substance Use: Individual Behaviour, Social Interactions, Markets and Politics, pages 215-228, Emerald Group Publishing Limited.
    7. Peter N. Golder & Gerard J. Tellis, 2004. "Growing, Growing, Gone: Cascades, Diffusion, and Turning Points in the Product Life Cycle," Marketing Science, INFORMS, vol. 23(2), pages 207-218, December.
    8. Yogendra Kumar & Runa Sarkar & Sanjeev Swami, 2009. "Cluster‐based diffusion: aggregate and disaggregate level modeling," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 6(1), pages 8-26, April.
    9. M Günther & C Stummer & L M Wakolbinger & M Wildpaner, 2011. "An agent-based simulation approach for the new product diffusion of a novel biomass fuel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(1), pages 12-20, January.
    10. Campbell, Amy R. & Ryley, Tim & Thring, Rob, 2012. "Identifying the early adopters of alternative fuel vehicles: A case study of Birmingham, United Kingdom," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1318-1327.
    11. Ryo Iwata & Kaoru Kuramoto & Kenyuu Matsumoto & Satoshi Kumagai, 2020. "Extracting Innovative Buyers by Scoring Using Innovator Theory," International Review of Management and Marketing, Econjournals, vol. 10(5), pages 92-102.
    12. Wagner A. Kamakura & Bruce S. Kossar & Michel Wedel, 2004. "Identifying Innovators for the Cross-Selling of New Products," Management Science, INFORMS, vol. 50(8), pages 1120-1133, August.
    Full references (including those not matched with items on IDEAS)

    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. Deepa Chandrasekaran & Gerard J. Tellis, 2008. "Global Takeoff of New Products: Culture, Wealth, or Vanishing Differences?," Marketing Science, INFORMS, vol. 27(5), pages 844-860, 09-10.
    2. 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.
    3. Qin, Ruwen & Nembhard, David A., 2012. "Demand modeling of stochastic product diffusion over the life cycle," International Journal of Production Economics, Elsevier, vol. 137(2), pages 201-210.
    4. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
    5. 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.
    6. 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.
    7. Sang-Gun Lee & Eui-bang Lee & Chang-Gyu Yang, 2014. "Strategies for ICT product diffusion: the case of the Korean mobile communications market," Service Business, Springer;Pan-Pacific Business Association, vol. 8(1), pages 65-81, March.
    8. Laciana, Carlos E. & Rovere, Santiago L. & Podestá, Guillermo P., 2013. "Exploring associations between micro-level models of innovation diffusion and emerging macro-level adoption patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(8), pages 1873-1884.
    9. Tolotti, Marco & Yepez, Jorge, 2020. "Hotelling-Bertrand duopoly competition under firm-specific network effects," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 105-128.
    10. Gil Appel & Eitan Muller, 2021. "Adoption patterns over time: a replication," Marketing Letters, Springer, vol. 32(4), pages 499-511, December.
    11. Franses, Philip Hans, 2021. "Modeling box office revenues of motion pictures✰," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    12. Liye Ma & Ramayya Krishnan & Alan L. Montgomery, 2015. "Latent Homophily or Social Influence? An Empirical Analysis of Purchase Within a Social Network," Management Science, INFORMS, vol. 61(2), pages 454-473, February.
    13. van Everdingen, Y.M. & Fok, D. & Stremersch, S., 2008. "Modeling Global Spill-Over of New Product Takeoff," ERIM Report Series Research in Management ERS-2008-067-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    15. 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.
    16. Jing Wang & Anocha Aribarg & Yves F. Atchadé, 2013. "Modeling Choice Interdependence in a Social Network," Marketing Science, INFORMS, vol. 32(6), pages 977-997, November.
    17. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.
    18. Samoilenko, Sergey & Osei-Bryson, Kweku-Muata, 2010. "Determining sources of relative inefficiency in heterogeneous samples: Methodology using Cluster Analysis, DEA and Neural Networks," European Journal of Operational Research, Elsevier, vol. 206(2), pages 479-487, October.
    19. Eryarsoy, Enes & Delen, Dursun & Davazdahemami, Behrooz & Topuz, Kazim, 2021. "A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19," Journal of Business Research, Elsevier, vol. 124(C), pages 163-178.
    20. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.

    More about this item

    Keywords

    Innovator Theory; Chasm theory; Agent-Based Modeling; Sensitivity Analysis; Three-Sigma Rule; O2O;
    All these keywords.

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • O39 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Other
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

    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:eco:journ3:2022-06-1. 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: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    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.