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The complexity of surveying web participation

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  • Page, Kelly L.
  • Uncles, Mark D.

Abstract

Marketing managers increasingly need to understand how consumers use and interact with digital media, such as the web, social networks, mobile phones, digital readers, search engines and so forth. The focus of this paper is web participation and the complexities involved in measuring usage and interaction with the web, particularly when relying on data from self-report surveys. Few studies have specifically examined how web participation can be surveyed—this paper addresses the gap. First, web participation is defined by drawing on studies of web usage in information systems, consumer and communication research, and audience use metrics in media research. Second, web participation is discussed as a complex multi-dimensional construct, something that is not fully acknowledged in existing web use research. Third, major influences on self-reported web participation are considered, in particular the impact of a consumer's web design experience and the person's perceptions of web usability on how they self-report web participation. These themes are investigated using a large web-based survey. Findings show that a consumer's perception of web usability has a significant impact on the self-reporting of web participation, but this depends on how web participation is measured and whether the individual has website design experience. The complexity of surveying web participation is apparent, as is the importance of understanding the web's usage-context.

Suggested Citation

  • Page, Kelly L. & Uncles, Mark D., 2014. "The complexity of surveying web participation," Journal of Business Research, Elsevier, vol. 67(11), pages 2356-2367.
  • Handle: RePEc:eee:jbrese:v:67:y:2014:i:11:p:2356-2367
    DOI: 10.1016/j.jbusres.2014.02.001
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    1. Sung S. Kim & Naresh K. Malhotra, 2005. "A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Postadoption Phenomena," Management Science, INFORMS, vol. 51(5), pages 741-755, May.
    2. Teo, Thompson S. H. & Lim, Vivien K. G. & Lai, Raye Y. C., 1999. "Intrinsic and extrinsic motivation in Internet usage," Omega, Elsevier, vol. 27(1), pages 25-37, February.
    3. Son, Minhee & Han, Kyesook, 2011. "Beyond the technology adoption: Technology readiness effects on post-adoption behavior," Journal of Business Research, Elsevier, vol. 64(11), pages 1178-1182.
    4. Barczak, Gloria & Ellen, Pam Scholder & Pilling, Bruce K., 1997. "Developing typologies of consumer motives for use of technologically based banking services," Journal of Business Research, Elsevier, vol. 38(2), pages 131-139, February.
    5. Ellis-Chadwick, Fiona & Doherty, Neil F., 2012. "Web advertising: The role of e-mail marketing," Journal of Business Research, Elsevier, vol. 65(6), pages 843-848.
    6. Eighmey, John & McCord, Lola, 1998. "Adding Value in the Information Age: Uses and Gratifications of Sites on the World Wide Web," Journal of Business Research, Elsevier, vol. 41(3), pages 187-194, March.
    7. Bucklin, Randolph E. & Sismeiro, Catarina, 2009. "Click Here for Internet Insight: Advances in Clickstream Data Analysis in Marketing," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 35-48.
    8. Sivadas, Eugene & Grewal, Rajdeep & Kellaris, James, 1998. "The Internet as a Micro Marketing Tool: Targeting Consumers through Preferences Revealed in Music Newsgroup Usage," Journal of Business Research, Elsevier, vol. 41(3), pages 179-186, March.
    9. Rice, Ronald E. & Katz, James E., 0. "Comparing internet and mobile phone usage: digital divides of usage, adoption, and dropouts," Telecommunications Policy, Elsevier, vol. 27(8-9), pages 597-623, September.
    10. 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.
    11. Kristine de Valck & Gerrit H. van Bruggen & Berendt Wierenga, 2009. "Virtual communities: A marketing perspective," Post-Print hal-00458421, HAL.
    12. Huh, Young Eun & Kim, Sang-Hoon, 2008. "Do early adopters upgrade early? Role of post-adoption behavior in the purchase of next-generation products," Journal of Business Research, Elsevier, vol. 61(1), pages 40-46, January.
    13. Porter, Constance Elise & Donthu, Naveen, 2006. "Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics," Journal of Business Research, Elsevier, vol. 59(9), pages 999-1007, September.
    14. Robert Kraut & Tridas Mukhopadhyay & Janusz Szczypula & Sara Kiesler & Bill Scherlis, 1999. "Information and Communication: Alternative Uses of the Internet in Households," Information Systems Research, INFORMS, vol. 10(4), pages 287-303, December.
    15. Zaichkowsky, Judith Lynne, 1985. "Measuring the Involvement Construct," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(3), pages 341-352, December.
    16. Thakor, Mrugank V. & Borsuk, Wendy & Kalamas, Maria, 2004. "Hotlists and Web browsing behavior--an empirical investigation," Journal of Business Research, Elsevier, vol. 57(7), pages 776-786, July.
    17. Patrali Chatterjee & Donna L. Hoffman & Thomas P. Novak, 2003. "Modeling the Clickstream: Implications for Web-Based Advertising Efforts," Marketing Science, INFORMS, vol. 22(4), pages 520-541, May.
    18. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    19. Olney, Thomas J & Holbrook, Morris B & Batra, Rajeev, 1991. "Consumer Responses to Advertising: The Effects of Ad Content, Emotions, and Attitude toward the Ad on Viewing Time," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 440-453, March.
    20. Desai, Kalpesh Kaushik & Hoyer, Wayne D, 2000. "Descriptive Characteristics of Memory-Based Consideration Sets: Influence of Usage Occasion Frequency and Usage Location Familiarity," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 27(3), pages 309-323, December.
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