IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v19y2000i1p22-42.html
   My bibliography  Save this article

Measuring the Customer Experience in Online Environments: A Structural Modeling Approach

Author

Listed:
  • Thomas P. Novak

    (eLab, Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203)

  • Donna L. Hoffman

    (eLab, Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203)

  • Yiu-Fai Yung

    (eLab, Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203, SAS Institute, Inc.)

Abstract

Intuition and previous research suggest that creating a compelling online environment for Web consumers will have numerous positive consequences for commercial Web providers. Online executives note that creating a compelling online experience for cyber customers is critical to creating competitive advantage on the Internet. Yet, very little is known about the factors that make using the Web a compelling experience for its users, and of the key consumer behavior outcomes of this compelling experience. Recently, the flow construct has been proposed as important for understanding consumer behavior on the World Wide Web, and as a way of defining the nature of compelling online experience. Although widely studied over the past 20 years, quantitative modeling efforts of the flow construct have been neither systematic nor comprehensive. In large parts, these efforts have been hampered by considerable confusion regarding the exact conceptual definition of flow. Lacking precise definition, it has been difficult to measure flow empirically, let alone apply the concept in practice. Following the conceptual model of flow proposed by Hoffman and Novak (1996), we conceptualize flow on the Web as a cognitive state experienced during navigation that is determined by (1) high levels of skill and control; (2) high levels of challenge and arousal; and (3) focused attention; and (4) is enhanced by interactivity and telepresence. Consumers who achieve flow on the Web are so acutely involved in the act of online navigation that thoughts and perceptions not relevant to navigation are screened out, and the consumer focuses entirely on the interaction. Concentration on the navigation experience is so intense that there is little attention left to consider anything else, and consequently, other events occurring in the consumer's surrounding physical environment lose significance. Self-consciousness disappears, the consumer's sense of time becomes distorted, and the state of mind arising as a result of achieving flow on the Web is extremely gratifying. In a quantitative modeling framework, we develop a structural model based on our previous conceptual model of flow that embodies the components of what makes for a compelling online experience. We use data collected from a largesample, Web-based consumer survey to measure these constructs, and we fit a series of structural equation models that test related prior theory. The conceptual model is largely supported, and the improved fit offered by the revised model provides additional insights into the direct and indirect influences of flow, as well as into the relationship of flow to key consumer behavior and Web usage variables. Our formulation provides marketing scientists with operational definitions of key model constructs and establishes reliability and validity in a comprehensive measurement framework. A key insight from the paper is that the degree to which the online experience is compelling can be defined, measured, and related well to important marketing variables. Our model constructs relate in significant ways to key consumer behavior variables, including online shopping and Web use applications such as the extent to which consumers search for product information and participate in chat rooms. As such, our model may be useful both theoretically and in practice as marketers strive to decipher the secrets of commercial success in interactive online environments.

Suggested Citation

  • Thomas P. Novak & Donna L. Hoffman & Yiu-Fai Yung, 2000. "Measuring the Customer Experience in Online Environments: A Structural Modeling Approach," Marketing Science, INFORMS, vol. 19(1), pages 22-42, May.
  • Handle: RePEc:inm:ormksc:v:19:y:2000:i:1:p:22-42
    DOI: 10.1287/mksc.19.1.22.15184
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.19.1.22.15184
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.19.1.22.15184?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
    ---><---

    References listed on IDEAS

    as
    1. Celsi, Richard L & Olson, Jerry C, 1988. "The Role of Involvement in Attention and Comprehension Processes," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 15(2), pages 210-224, September.
    2. Dellaert, B.G.C. & Kahn, B., 1998. "How Tolerable is Delay? Consumers' Evaluations of Internet Web Sites After Waiting," Other publications TiSEM ca8d3a6b-4329-42ae-a595-9, Tilburg University, School of Economics and Management.
    3. Havlena, William J & Holbrook, Morris B, 1986. "The Varieties of Consumption Experience: Comparing Two Typologies of Emotion in Consumer Behavior," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 13(3), pages 394-404, December.
    4. Mitchell, Andrew A & Dacin, Peter A, 1996. "The Assessment of Alternative Measures of Consumer Expertise," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 23(3), pages 219-239, December.
    5. 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)

    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. 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.
    2. Huang, Liyuan & Gursoy, Dogan & Xu, Honggang, 2014. "Impact of personality traits and involvement on prior knowledge," Annals of Tourism Research, Elsevier, vol. 48(C), pages 42-57.
    3. Pham, Thi Song Hanh & Ahammad, Mohammad Faisal, 2017. "Antecedents and consequences of online customer satisfaction: A holistic process perspective," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 332-342.
    4. Viswanath Venkatesh & Ritu Agarwal, 2006. "Turning Visitors into Customers: A Usability-Centric Perspective on Purchase Behavior in Electronic Channels," Management Science, INFORMS, vol. 52(3), pages 367-382, March.
    5. Ashish Agarwal & Tridas Mukhopadhyay, 2016. "The Impact of Competing Ads on Click Performance in Sponsored Search," Information Systems Research, INFORMS, vol. 27(3), pages 538-557.
    6. Beuckels, Emma & Hudders, Liselot, 2016. "An experimental study to investigate the impact of image interactivity on the perception of luxury in an online shopping context," Journal of Retailing and Consumer Services, Elsevier, vol. 33(C), pages 135-142.
    7. Shirai, Miyuri, 2009. "Investigation of emotional responses to an unexpected price," Australasian marketing journal, Elsevier, vol. 17(1), pages 2-8.
    8. Wirawan Dony Dahana & HeeJae Shin & Sotaro Katsumata, 2018. "Influence of individual characteristics on whether and how much consumers engage in showrooming behavior," Electronic Commerce Research, Springer, vol. 18(4), pages 665-692, December.
    9. Wu, Lei-Yu & Chen, Kuan-Yang & Chen, Po-Yuan & Cheng, Shu-Ling, 2014. "Perceived value, transaction cost, and repurchase-intention in online shopping: A relational exchange perspective," Journal of Business Research, Elsevier, vol. 67(1), pages 2768-2776.
    10. Gupta, Pranjal & Yadav, Manjit S. & Varadarajan, Rajan, 2009. "How Task-Facilitative Interactive Tools Foster Buyers’ Trust in Online Retailers: A Process View of Trust Development in the Electronic Marketplace," Journal of Retailing, Elsevier, vol. 85(2), pages 159-176.
    11. Grossman, Ori & Rachamim, Matti, 2024. "How can coffee shops draw customers back after COVID-19? the influence of psychological distance on coffee versus tea preference," Journal of Business Research, Elsevier, vol. 172(C).
    12. John Hadjimarcou, 2012. "An Investigation Of Informational Versus Emotional Advertising Appeals During Life Transitions," International Journal of Management and Marketing Research, The Institute for Business and Finance Research, vol. 5(1), pages 55-65.
    13. Manning, Kenneth C. & Sprott, David E. & Miyazaki, Anthony D., 2003. "Unit price usage knowledge: Conceptualization and empirical assessment," Journal of Business Research, Elsevier, vol. 56(5), pages 367-377, May.
    14. März, Armin & Lachner, Michael & Heumann, Christian G. & Schumann, Jan H. & von Wangenheim, Florian, 2021. "How You Remind Me! The Influence of Mobile Push Notifications on Success Rates in Last-Minute Bidding," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 11-24.
    15. Bryce, Cormac & Dowling, Michael & Lucey, Brian, 2020. "The journal quality perception gap," Research Policy, Elsevier, vol. 49(5).
    16. Shun-Yang Lee & Julian Runge & Daniel Yoo & Yakov Bart & Anett Gyurak & J. W. Schneider, 2023. "COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?," Papers 2307.09035, arXiv.org, revised Jan 2024.
    17. Oliver Hinz & Jochen Eckert, 2010. "The Impact of Search and Recommendation Systems on Sales in Electronic Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 2(2), pages 67-77, April.
    18. Milou Kievik & Ellen F.J. ter Huurne & Jan M. Gutteling, 2012. "The action suited to the word? Use of the framework of risk information seeking to understand risk-related behaviors," Journal of Risk Research, Taylor & Francis Journals, vol. 15(2), pages 131-147, February.
    19. Ingrid M. Martin & Holly Bender & Carol Raish, 2007. "What Motivates Individuals to Protect Themselves from Risks: The Case of Wildland Fires," Risk Analysis, John Wiley & Sons, vol. 27(4), pages 887-900, August.
    20. O'Cass, A., 2000. "An assessment of consumers product, purchase decision, advertising and consumption involvement in fashion clothing," Journal of Economic Psychology, Elsevier, vol. 21(5), pages 545-576, October.

    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:inm:ormksc:v:19:y:2000:i:1:p:22-42. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.