IDEAS home Printed from https://ideas.repec.org/p/ems/eureri/17766.html
   My bibliography  Save this paper

Deviation Among Technology Reviews: An Informative Enrichment of Technology Evolution Theory for Marketing

Author

Listed:
  • Sood, A.
  • Stremersch, S.

Abstract

Understanding technological change is of critical importance to marketers, as it bears new markets, new brands, new customers, and new market leaders. This paper examines the deviation among reviews of a technology’s performance and its consequences for inferences on technology evolution patterns. The basic premise of the current paper is that technology evolution literature, while highly relevant, is misguided in that it ignores potential deviation among technology reviews. Using a comprehensive dataset of all published reviews, both before and after FDA approval, of 7 statins for cholesterol reduction (LDL) from 1982 to 2007, the authors find that: (1) there exists vast deviation among reviews of technology performance leading to systematic bias in the portrayal of the path of technology evolution, especially if one relies only on manufacturer’s claims, (2) such deviation does not fade over time, (3) technology review (study design) characteristics affect the stated performance and, (4) both higher technology performance and a higher deviation affect sales positively, also when one controls for a firm’s marketing expenditures. We discuss the implications of these findings for technology evolution theory, managerial practice and public policy.

Suggested Citation

  • Sood, A. & Stremersch, S., 2010. "Deviation Among Technology Reviews: An Informative Enrichment of Technology Evolution Theory for Marketing," ERIM Report Series Research in Management ERS-2010-005-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.
    • Handle: RePEc:ems:eureri:17766
    as

    Download full text from publisher

    File URL: https://repub.eur.nl/pub/17766/ERS-2010-005-MKT.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.
    2. Dhar, Ravi, 1997. "Consumer Preference for a No-Choice Option," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 24(2), pages 215-231, September.
    3. Debanjan Mitra & Peter N. Golder, 2006. "How Does Objective Quality Affect Perceived Quality? Short-Term Effects, Long-Term Effects, and Asymmetries," Marketing Science, INFORMS, vol. 25(3), pages 230-247, 05-06.
    4. Yelkur, Rama & Morrison, Janet & Steiner, Erwin H. & Schmehl, Ian, 2001. "Product liability: Its impact on the auto industry, Consumers, and global competitiveness," Business Horizons, Elsevier, vol. 44(2), pages 61-66.
    5. Venkataraman, S. & Stremersch, S., 2007. "The Debate on Influencing Doctors’ Decisions: Are Drug Characteristics the Missing Link?," ERIM Report Series Research in Management ERS-2007-056-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.
    6. Peter N. Golder & Gerard J. Tellis, 1997. "Will It Every Fly? Modeling the Takeoff of Really New Consumer Durables," Marketing Science, INFORMS, vol. 16(3), pages 256-270.
    7. Suarez, Fernando F., 2004. "Battles for technological dominance: an integrative framework," Research Policy, Elsevier, vol. 33(2), pages 271-286, March.
    8. Sriram Venkataraman & Stefan Stremersch, 2007. "The Debate on Influencing Doctors' Decisions: Are Drug Characteristics the Missing Link?," Management Science, INFORMS, vol. 53(11), pages 1688-1701, November.
    9. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
    10. Katz, Michael L & Shapiro, Carl, 1985. "Network Externalities, Competition, and Compatibility," American Economic Review, American Economic Association, vol. 75(3), pages 424-440, June.
    11. Bates, Hilary & Holweg, Matthias & Lewis, Michael & Oliver, Nick, 2007. "Motor vehicle recalls: Trends, patterns and emerging issues," Omega, Elsevier, vol. 35(2), pages 202-210, April.
    12. Pierre Azoulay, 2002. "Do Pharmaceutical Sales Respond to Scientific Evidence?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 11(4), pages 551-594, December.
    13. Coscelli, Andrea & Shum, Matthew, 2004. "An empirical model of learning and patient spillovers in new drug entry," Journal of Econometrics, Elsevier, vol. 122(2), pages 213-246, October.
    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. Verniers, Isabel & Stremersch, Stefan & Croux, Christophe, 2011. "The global entry of new pharmaceuticals: A joint investigation of launch window and price," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 295-308.

    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. Bradley T. Shapiro, 2018. "Informational Shocks, Off-Label Prescribing, and the Effects of Physician Detailing," Management Science, INFORMS, vol. 64(12), pages 5925-5945, December.
    2. Katharina Elisabeth Blankart & Tom Stargardt, 2020. "The impact of drug quality ratings from health technology assessments on the adoption of new drugs by physicians in Germany," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 63-82, October.
    3. Pradeep Chintagunta & Renna Jiang & Ginger Jin, 2009. "Information, learning, and drug diffusion: The case of Cox-2 inhibitors," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 399-443, December.
    4. Nuno Camacho & Bas Donkers & Stefan Stremersch, 2011. "Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality," Marketing Science, INFORMS, vol. 30(2), pages 305-320, 03-04.
    5. Ajay Bhaskarabhatla & Priyatam Anurag & Chirantan Chatterjee & Enrico Pennings, 2021. "How Does Regulation Impact Strategic Repositioning by Firms Across Submarkets? Evidence from the Indian Pharmaceutical Industry," Strategy Science, INFORMS, vol. 6(3), pages 209-227, September.
    6. Castanheira, Micael & Ornaghi, Carmine & Siotis, Georges, 2019. "The unexpected consequences of generic entry," Journal of Health Economics, Elsevier, vol. 68(C).
    7. Anusua Datta & Dhaval Dave, 2017. "Effects of Physician‐directed Pharmaceutical Promotion on Prescription Behaviors: Longitudinal Evidence," Health Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 450-468, April.
    8. Hongju Liu & Qiang Liu & Pradeep K. Chintagunta, 2017. "Promotion Spillovers: Drug Detailing in Combination Therapy," Marketing Science, INFORMS, vol. 36(3), pages 382-401, May.
    9. Kaplan, Sarah & Tripsas, Mary, 2008. "Thinking about technology: Applying a cognitive lens to technical change," Research Policy, Elsevier, vol. 37(5), pages 790-805, June.
    10. Sood, Ashish & Kappe, Eelco & Stremersch, Stefan, 2014. "The commercial contribution of clinical studies for pharmaceutical drugs," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 65-77.
    11. Kremer, Sara T.M. & Bijmolt, Tammo H.A. & Leeflang, Peter S.H. & Wieringa, Jaap E., 2008. "Generalizations on the effectiveness of pharmaceutical promotional expenditures," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 234-246.
    12. Andrew J. Epstein & Jonathan D. Ketcham, 2014. "Information technology and agency in physicians' prescribing decisions," RAND Journal of Economics, RAND Corporation, vol. 45(2), pages 422-448, June.
    13. van de Kaa, Geerten & van Ek, Martijn & Kamp, Linda M. & Rezaei, Jafar, 2020. "Wind turbine technology battles: Gearbox versus direct drive - opening up the black box of technology characteristics," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    14. Camacho, Nuno & De Jong, Martijn & Stremersch, Stefan, 2014. "The effect of customer empowerment on adherence to expert advice," International Journal of Research in Marketing, Elsevier, vol. 31(3), pages 293-308.
    15. Stefan Stremersch & Jorge Gonzalez & Albert Valenti & Julian Villanueva, 2023. "The value of context-specific studies for marketing," Journal of the Academy of Marketing Science, Springer, vol. 51(1), pages 50-65, January.
    16. Narayanan, V.K. & Chen, Tianxu, 2012. "Research on technology standards: Accomplishment and challenges," Research Policy, Elsevier, vol. 41(8), pages 1375-1406.
    17. Andrew Ching & Masakazu Ishihara, 2010. "The effects of detailing on prescribing decisions under quality uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 123-165, June.
    18. David B. Ridley, 2015. "Payments, Promotion, And The Purple Pill," Health Economics, John Wiley & Sons, Ltd., vol. 24(1), pages 86-103, January.
    19. McKibbin, Rebecca, 2023. "The effect of RCTs on drug demand: Evidence from off-label cancer drugs," Journal of Health Economics, Elsevier, vol. 90(C).
    20. Greffion, Jérôme & Breda, Thomas, 2015. "Façonner la prescription, influencer les médecins," Revue de la Régulation - Capitalisme, institutions, pouvoirs, Association Recherche et Régulation, vol. 17.

    More about this item

    Keywords

    detailing; innovation; marketing; performance; reviews; sales; statins; technology evolution;
    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
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

    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:ems:eureri:17766. 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: RePub (email available below). General contact details of provider: https://edirc.repec.org/data/erimanl.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.