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Evaluating Shopbots' Market Representativeness in Online Research: A Comparative Study

This research study explores the differences in market representativeness among popular shopbots and their implications for Internet research. With a focus on 8 shopbots and over 2.2 million price observations from 84 vendors over 120 days, the study investigates the equality of shopbots and the attributes that impact their representativeness. Using regression analysis, the findings reveal that market coverage and vendor affiliation play crucial roles in determining a shopbot's representativeness. The study concludes that not all shopbots are created equal and emphasizes the importance of choosing the right shopbots for academic research.

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Evaluating Shopbots' Market Representativeness in Online Research: A Comparative Study

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  1. Shopbot Market Representativeness Gove N. Allen Jianan Wu A. B. Freeman School of Business Tulane University

  2. Research Questions • Do all shopbots represent markets equally? • What attributes to shopbots’ market representativeness? • What are the implications of shopbot market representativeness for Internet research?

  3. Research Using Shopbots

  4. Current Study • 450+ Books • 120 days • 8 shopbots (Addall, Bizrate, Dealtime, ISBN.nu, Mysimon, Pricegrabber, Pricescan, shopping.yahoo) • 84 different vendors • 2.2 million price observations

  5. Are Shopbots Created Equal?

  6. Are Shopbots Created Equal?

  7. What Attributes to Shopbot Representativeness?

  8. What Attributes to Shopbot Representativeness?

  9. Regression Model

  10. Regression Results

  11. Does It Really Matter?Combining Shopbots

  12. Conclusions • All Shopbots are not created equal. • Both market coverage and vendor affiliation determine how representative a shopbot is. • When selecting shopbots for academic research, “Choose Wisely”

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