IDEAS home Printed from https://ideas.repec.org/a/ush/jaessh/v4y2009i2(8)_summer200962.html
   My bibliography  Save this article

Modeling The Effect Of Belief Revisions On The Success Of Co-Branding

Author

Listed:
  • Chia-Lin LEE
  • Reinhold DECKER

Abstract

This paper provides a normative guideline regarding the successful formation of co-branding alliances for both academic researchers and practitioners. We use the expectancy-value model to quantify the mechanism of belief revision in co-branding. Starting from this, an existing mathematical model is adapted in order to investigate (1) the influence of belief revisions on the necessary condition of a successful co-branding alliance (i.e., a sufficient amount of required expansion for the partnering brands) and (2) the existence of an ideal situation that ensures the success. The resulting propositions show that belief revisions can affect a brand�s intention with respect to a co-branding partnership. A simulation study demonstrates that an ideal situation exists when the partnering brands are similar in the magnitude of customers� belief revision, brand reputation, and customer loyalty. The present paper advances existing knowledge by relating the success of co-branding partnerships to consumer evaluations. Managerial implications and future research directions are also discussed.

Suggested Citation

  • Chia-Lin LEE & Reinhold DECKER, 2009. "Modeling The Effect Of Belief Revisions On The Success Of Co-Branding," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(2(8)_ Sum).
    • Handle: RePEc:ush:jaessh:v:4:y:2009:i:2(8)_summer2009:62
    as

    Download full text from publisher

    File URL: http://www.jaes.reprograph.ro/articles/summer2009/LeeCH_DeckerR.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John R. Hauser & Steven M. Shugan, 2008. "Defensive Marketing Strategies," Marketing Science, INFORMS, vol. 27(1), pages 88-110, 01-02.
    2. Agarwal, James & Malhotra, Naresh K., 2005. "An integrated model of attitude and affect: Theoretical foundation and an empirical investigation," Journal of Business Research, Elsevier, vol. 58(4), pages 483-493, April.
    3. MacKenzie, Scott B, 1986. "The Role of Attention in Mediating the Effect of Advertising on Attribute Importance," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(2), pages 174-195, September.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Fazio, Russell H & Powell, Martha C & Williams, Carol J, 1989. "The Role of Attitude Accessibility in the Attitude-to-Behavior Process," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 16(3), pages 280-289, December.
    6. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    7. Tansev Geylani & J. Jeffrey Inman & Frenkel Ter Hofstede, 2008. "Image Reinforcement or Impairment: The Effects of Co-Branding on Attribute Uncertainty," Marketing Science, INFORMS, vol. 27(4), pages 730-744, 07-08.
    8. Michael Spence, 1973. "Job Market Signaling," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 87(3), pages 355-374.
    9. Sandra J. Milberg, 2001. "Positive Feedback Effects Of Brand Extensions: Expanding Brand Meaning And The Range Of Extendibility," Abante, Escuela de Administracion. Pontificia Universidad Católica de Chile., vol. 4(1), pages 3-35.
    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. Inseong Song & Pradeep Chintagunta, 2003. "A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category," Quantitative Marketing and Economics (QME), Springer, vol. 1(4), pages 371-407, December.
    2. Kannan Srinivasan, 2006. "—Empirical Analysis of Theory-Based Models in Marketing," Marketing Science, INFORMS, vol. 25(6), pages 635-637, 11-12.
    3. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.
    4. Lee, Boon-Chye & Ang, Lawrence & Dubelaar, Chris, 2004. "Lemons on the Web: A Signalling Approach to the Problem of Trust in Internet Commerce," Economics Working Papers wp04-10, School of Economics, University of Wollongong, NSW, Australia.
    5. Saha, Sajeeb & Ranjan, Kumar Rakesh & Pappu, Ravi & Akhlaghpour, Saeed, 2023. "Corporate giving and its impact on consumer evaluations: A meta-analysis," Journal of Business Research, Elsevier, vol. 158(C).
    6. Steven M. Shugan, 2002. "Editorial: Marketing Science, Models, Monopoly Models, and Why We Need Them," Marketing Science, INFORMS, vol. 21(3), pages 223-228.
    7. Pradeep K. Chintagunta & Harikesh S. Nair, 2011. "Structural Workshop Paper --Discrete-Choice Models of Consumer Demand in Marketing," Marketing Science, INFORMS, vol. 30(6), pages 977-996, November.
    8. Qu, Yingge & Kumar, V. & Zhao, Yi, 2023. "A dynamic model of the contract length and early termination: The roles of technology evolution and pricing strategy," Journal of Business Research, Elsevier, vol. 167(C).
    9. Bart Bronnenberg & Jean Dubé & Carl Mela & Paulo Albuquerque & Tulin Erdem & Brett Gordon & Dominique Hanssens & Guenter Hitsch & Han Hong & Baohong Sun, 2008. "Measuring long-run marketing effects and their implications for long-run marketing decisions," Marketing Letters, Springer, vol. 19(3), pages 367-382, December.
    10. Xenia Raufeisen & Linda Wulf & Sören Köcher & Ulya Faupel & Hartmut H. Holzmüller, 2019. "Spillover effects in marketing: integrating core research domains," AMS Review, Springer;Academy of Marketing Science, vol. 9(3), pages 249-267, December.
    11. Ngobo, Paul-Valentin & Legohérel, Patrick & Guéguen, Nicolas, 2010. "A cross-category investigation into the effects of nine-ending pricing on brand choice," Journal of Retailing and Consumer Services, Elsevier, vol. 17(5), pages 374-385.
    12. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    13. Theoharakis, Vasilis & Vakratsas, Demetrios & Wong, Veronica, 2007. "Market-level information and the diffusion of competing technologies: An exploratory analysis of the LAN industry," Research Policy, Elsevier, vol. 36(5), pages 742-757, June.
    14. Zhang, Jun & Guo, Ruey-Shan, 2016. "The D-Day, V-Day, and bleak days of a disruptive technology: A new model for ex-ante evaluation of the timing of technology disruptionAuthor-Name: Chen, Chialin," European Journal of Operational Research, Elsevier, vol. 251(2), pages 562-574.
    15. Brett R. Gordon, 2009. "A Dynamic Model of Consumer Replacement Cycles in the PC Processor Industry," Marketing Science, INFORMS, vol. 28(5), pages 846-867, 09-10.
    16. Meeran, Sheik & Jahanbin, Semco & Goodwin, Paul & Quariguasi Frota Neto, Joao, 2017. "When do changes in consumer preferences make forecasts from choice-based conjoint models unreliable?," European Journal of Operational Research, Elsevier, vol. 258(2), pages 512-524.
    17. Krishnan, Trichy V. & Feng, Shanfei & Beebe, Tony, 2011. "Modeling the demand and supply in a new B2B-upstream market using a knowledge updating process," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1160-1177, October.
    18. Byung Cheol Lee & Christine Moorman & C. Page Moreau & Andrew T. Stephen & Donald R. Lehmann, 2020. "The past, present, and future of innovation research," Marketing Letters, Springer, vol. 31(2), pages 187-198, September.
    19. J. Miguel Villas-Boas, 2000. "Competing with Experience Goods," Econometric Society World Congress 2000 Contributed Papers 0771, Econometric Society.
    20. 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.

    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:ush:jaessh:v:4:y:2009:i:2(8)_summer2009:62. 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: Laura Stefanescu (email available below). General contact details of provider: https://edirc.repec.org/data/fmuspro.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.