IDEAS home Printed from https://ideas.repec.org/a/eee/joinma/v25y2011i2p95-109.html
   My bibliography  Save this article

Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?

Author

Listed:
  • Drechsler, Wenzel
  • Natter, Martin

Abstract

Online price comparison sites (shopbots) like PriceGrabber.com are the most powerful tools for consumers to easily compare prices and find offers for desired products. Besides providing distributions of actual prices in price comparison tables, shopbots like NexTag.com have recently introduced price charts (line charts) displaying a product's full price history. Price charts should support consumers in forming expectations about future prices. Nevertheless, it is currently unclear how price charts influence consumer price expectations and purchase decisions. The results of this study show that the provision of past prices leads to strong adjustments of price expectations depending on price chart characteristics. In particular, the trend, variance and range of past prices in the chart strongly affect price expectations and purchase timing decisions. Furthermore, in the case of a strong downward trend and high variance in past prices, results show that nearly 50% of the total effect is caused by the visualization of the price history.

Suggested Citation

  • Drechsler, Wenzel & Natter, Martin, 2011. "Do Price Charts Provided by Online Shopbots Influence Price Expectations and Purchase Timing Decisions?," Journal of Interactive Marketing, Elsevier, vol. 25(2), pages 95-109.
    • Handle: RePEc:eee:joinma:v:25:y:2011:i:2:p:95-109
    DOI: 10.1016/j.intmar.2011.02.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1094996811000119
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.intmar.2011.02.001?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. Barry L. Bayus, 1998. "An Analysis of Product Lifetimes in a Technologically Dynamic Industry," Management Science, INFORMS, vol. 44(6), pages 763-775, June.
    2. Ganesh Iyer & Amit Pazgal, 2003. "Internet Shopping Agents: Virtual Co-Location and Competition," Marketing Science, INFORMS, vol. 22(1), pages 85-106, November.
    3. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    4. Seetharaman, P B & Chintagunta, Pradeep K, 2003. "The Proportional Hazard Model for Purchase Timing: A Comparison of Alternative Specifications," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 368-382, July.
    5. Sudip Bhattacharjee & Ram D. Gopal & Kaveepan Lertwachara & James R. Marsden & Rahul Telang, 2007. "The Effect of Digital Sharing Technologies on Music Markets: A Survival Analysis of Albums on Ranking Charts," Management Science, INFORMS, vol. 53(9), pages 1359-1374, September.
    6. Shlomo Benartzi & Richard H. Thaler, 1999. "Risk Aversion or Myopia? Choices in Repeated Gambles and Retirement Investments," Management Science, INFORMS, vol. 45(3), pages 364-381, March.
    7. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
    8. Eileen Bridges & Chi Kin (Bennett) Yim & Richard A. Briesch, 1995. "A High-Tech Product Market Share Model with Customer Expectations," Marketing Science, INFORMS, vol. 14(1), pages 61-81.
    9. Kalyanaram, Gurumurthy & Little, John D C, 1994. "An Empirical Analysis of Latitude of Price Acceptance in Consumer Package Goods," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 21(3), pages 408-418, December.
    10. Urbany, Joel E & Bearden, William O & Weilbaker, Dan C, 1988. "The Effect of Plausible and Exaggerated Reference Prices on Consumer Perceptions and Price Search," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(1), pages 95-110, June.
    11. Russell S. Winer, 1985. "Technical Note—A Price Vector Model of Demand for Consumer Durables: Preliminary Developments," Marketing Science, INFORMS, vol. 4(1), pages 74-90.
    12. Rao, Akshay R & Sieben, Wanda A, 1992. "The Effect of Prior Knowledge on Price Acceptability and the Type of Information Examined," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 19(2), pages 256-270, September.
    13. 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.
    14. Roehm, Michelle L & Sternthal, Brian, 2001. "The Moderating Effect of Knowledge and Resources on the Persuasive Impact of Analogies," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 257-272, September.
    15. Subramanian Balachander & Kannan Srinivasan, 1998. "Modifying Customer Expectations of Price Decreases for a Durable Product," Management Science, INFORMS, vol. 44(6), pages 776-786, June.
    16. Ganesh Iyer & Amit Pazgal, 2003. "Erratum: Internet Shopping Agents: Virtual Co-Location and Competition," Marketing Science, INFORMS, vol. 22(2), pages 271-271, November.
    17. Tülin Erdem & Michael Keane & T. Öncü & Judi Strebel, 2005. "Learning About Computers: An Analysis of Information Search and Technology Choice," Quantitative Marketing and Economics (QME), Springer, vol. 3(3), pages 207-247, September.
    18. Janiszewski, Chris & Lichtenstein, Donald R, 1999. "A Range Theory Account of Price Perception," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(4), pages 353-368, March.
    19. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
    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. Martin Spann & Bernd Skiera, 2020. "Dynamische Preisgestaltung in der digitalisierten Welt [Dynamic Pricing in a Digitized World]," Schmalenbach Journal of Business Research, Springer, vol. 72(3), pages 321-342, September.
    2. Prakash, David & Spann, Martin, 2022. "Dynamic pricing and reference price effects," Journal of Business Research, Elsevier, vol. 152(C), pages 300-314.
    3. Andrea Runfola & Massimo Rosati & Simone Guercini, 2013. "New business models in online hotel distribution: emerging private sales versus leading IDS," Service Business, Springer;Pan-Pacific Business Association, vol. 7(2), pages 183-205, June.
    4. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.
    5. Renatas Špicas & Airidas Neifaltas & Rasa Kanapickienė & Greta Keliuotytė-Staniulėnienė & Deimantė Vasiliauskaitė, 2023. "Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform," Risks, MDPI, vol. 11(7), pages 1-30, July.
    6. Zheng Gong & Jin Huang & Yuxin Chen, 2022. "What the Past Tells About the Future: Historical Prices in the Durable Goods Market," Management Science, INFORMS, vol. 68(12), pages 8857-8871, December.
    7. Viglia, Giampaolo & Abrate, Graziano, 2014. "How social comparison influences reference price formation in a service context," Journal of Economic Psychology, Elsevier, vol. 45(C), pages 168-180.

    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. Jaikumar, Saravana & Sahay, Arvind, 2016. "Effect of Overlapping Price Ranges on Price Perception: Revisiting the Range Theory of Price Perception," IIMA Working Papers WP2016-02-02, Indian Institute of Management Ahmedabad, Research and Publication Department.
    2. Lillian L. Cheng & Kent B. Monroe, 2013. "An appraisal of behavioral price research (part 1): price as a physical stimulus," AMS Review, Springer;Academy of Marketing Science, vol. 3(3), pages 103-129, September.
    3. Sheehan, Daniel & Hardesty, David M. & Ziegler, Alexander H. & Chen, Haipeng (Allan), 2019. "Consumer reactions to price discounts across online shopping experiences," Journal of Retailing and Consumer Services, Elsevier, vol. 51(C), pages 129-138.
    4. Gavan J. Fitzsimons & Donald R. Lehmann, 2004. "Reactance to Recommendations: When Unsolicited Advice Yields Contrary Responses," Marketing Science, INFORMS, vol. 23(1), pages 82-94, September.
    5. Anthony Dukes & Lin Liu, 2016. "Online Shopping Intermediaries: The Strategic Design of Search Environments," Management Science, INFORMS, vol. 62(4), pages 1064-1077, April.
    6. Yasemin Boztug & Lutz Hildebrandt, 2005. "An empirical test of theories of price valuation using a semiparametric approach, reference prices, and accounting for heterogeneity," SFB 649 Discussion Papers SFB649DP2005-057, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    7. Mantin, Benny & Gillen, David, 2011. "The hidden information content of price movements," European Journal of Operational Research, Elsevier, vol. 211(2), pages 385-393, June.
    8. Sahar Karimi, 2021. "Cross-visiting Behaviour of Online Consumers Across Retailers’ and Comparison Sites, a Macro-Study," Information Systems Frontiers, Springer, vol. 23(3), pages 531-542, June.
    9. Grewal, Dhruv & Janakiraman, Ramkumar & Kalyanam, Kirthi & Kannan, P.K. & Ratchford, Brian & Song, Reo & Tolerico, Stephen, 2010. "Strategic Online and Offline Retail Pricing: A Review and Research Agenda," Journal of Interactive Marketing, Elsevier, vol. 24(2), pages 138-154.
    10. Kalyanaram, Gurumurthy & Winer, Russell S., 2022. "Behavioral response to price: Data-based insights and future research for retailing," Journal of Retailing, Elsevier, vol. 98(1), pages 46-70.
    11. Alan L. Montgomery & Kartik Hosanagar & Ramayya Krishnan & Karen B. Clay, 2004. "Designing a Better Shopbot," Management Science, INFORMS, vol. 50(2), pages 189-206, February.
    12. De Bruyn, Arnaud & Prokopec, Sonja, 2017. "Assimilation-contrast theory in action: Operationalization and managerial impact in a fundraising context," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 367-381.
    13. Caputo, Vincenzina & Lusk, Jayson L. & Nayga, Rodolfo M., 2018. "Choice experiments are not conducted in a vacuum: The effects of external price information on choice behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 335-351.
    14. Dost, Florian & Wilken, Robert, 2012. "Measuring willingness to pay as a range, revisited: When should we care?," International Journal of Research in Marketing, Elsevier, vol. 29(2), pages 148-166.
    15. Mantin, Benny & Rubin, Eran, 2018. "Price volatility and market performance measures: The case of revenue managed goods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 120(C), pages 35-50.
    16. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    17. Ranyard, Rob & Missier, Fabio Del & Bonini, Nicolao & Duxbury, Darren & Summers, Barbara, 2008. "Perceptions and expectations of price changes and inflation: A review and conceptual framework," Journal of Economic Psychology, Elsevier, vol. 29(4), pages 378-400, August.
    18. René Algesheimer & Sharad Borle & Utpal M. Dholakia & Siddharth S. Singh, 2010. "The Impact of Customer Community Participation on Customer Behaviors: An Empirical Investigation," Marketing Science, INFORMS, vol. 29(4), pages 756-769, 07-08.
    19. Weisstein, Fei L. & Kukar-Kinney, Monika & Monroe, Kent B., 2016. "Determinants of consumers' response to pay-what-you-want pricing strategy on the Internet," Journal of Business Research, Elsevier, vol. 69(10), pages 4313-4320.
    20. Igari, Ryosuke & Hoshino, Takahiro, 2018. "A Bayesian data combination approach for repeated durations under unobserved missing indicators: Application to interpurchase-timing in marketing," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 150-166.

    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:joinma:v:25:y:2011:i:2:p:95-109. 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: https://www.journals.elsevier.com/journal-of-interactive-marketing/ .

    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.