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Web Trends: A valuable tool for business research

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  • France, Stephen L.
  • Shi, Yuying
  • Kazandjian, Brett

Abstract

Web search data are a valuable source of business and economic information. Previous studies have utilized Google Trends web search data for economic forecasting. We expand this work by creating an algorithm to combine and aggregate search volume data, so that the resulting data are both consistent over time and consistent between data series. We give a brand equity example, where Google Trends is used to create several brand equity series for 100 top-ranked brands. We validate these series by correlating them with the Interbrand brand equity index and by testing how well the series components predict company revenue. We give a managerial business research example, where Google Trends data are implemented as a measure of consumer “buzz” and is used to improve pre-release predictions of movie revenue.

Suggested Citation

  • France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
  • Handle: RePEc:eee:jbrese:v:132:y:2021:i:c:p:666-679
    DOI: 10.1016/j.jbusres.2020.10.019
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    References listed on IDEAS

    as
    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
    2. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    3. Hamid, Alain & Heiden, Moritz, 2015. "Forecasting volatility with empirical similarity and Google Trends," Journal of Economic Behavior & Organization, Elsevier, vol. 117(C), pages 62-81.
    4. Karniouchina, Ekaterina V., 2011. "Impact of star and movie buzz on motion picture distribution and box office revenue," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 62-74.
    5. Francisca Beer & Fabrice Hervé & Mohamed Zouaoui, 2013. "Is Big Brother Watching Us? Google, Investor Sentiment and the Stock Market," Economics Bulletin, AccessEcon, vol. 33(1), pages 454-466.
    6. Martin Spann & Bernd Skiera, 2003. "Internet-Based Virtual Stock Markets for Business Forecasting," Management Science, INFORMS, vol. 49(10), pages 1310-1326, October.
    7. Plaza, Beatriz, 2011. "Google Analytics for measuring website performance," Tourism Management, Elsevier, vol. 32(3), pages 477-481.
    8. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    9. Chetna Kudeshia & Amresh Kumar, 2017. "Social eWOM: does it affect the brand attitude and purchase intention of brands?," Management Research Review, Emerald Group Publishing Limited, vol. 40(3), pages 310-330, March.
    10. Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
    11. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    12. Bruno Godey & Aikaterini Manthiou & Daniele Pederzoli & Joonas Rokka & Gaetano Aiello & Raffaele Donvito & Rahul Singh, 2016. "Social media marketing efforts of luxury brands : Influence on brand equity and consumer behavior," Post-Print hal-02313434, HAL.
    13. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    14. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    15. Divakaran, Pradeep Kumar Ponnamma & Palmer, Adrian & Søndergaard, Helle Alsted & Matkovskyy, Roman, 2017. "Pre-launch Prediction of Market Performance for Short Lifecycle Products Using Online Community Data," Journal of Interactive Marketing, Elsevier, vol. 38(C), pages 12-28.
    16. Liwen Vaughan & Yue Chen, 2015. "Data mining from web search queries: A comparison of google trends and baidu index," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(1), pages 13-22, January.
    17. Guiyang Xiong & Sundar Bharadwaj, 2014. "Prerelease Buzz Evolution Patterns and New Product Performance," Marketing Science, INFORMS, vol. 33(3), pages 401-421, May.
    18. Anita Elberse & Jehoshua Eliashberg, 2003. "Demand and Supply Dynamics for Sequentially Released Products in International Markets: The Case of Motion Pictures," Marketing Science, INFORMS, vol. 22(3), pages 329-354.
    19. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    20. Ramya Neelamegham & Pradeep Chintagunta, 1999. "A Bayesian Model to Forecast New Product Performance in Domestic and International Markets," Marketing Science, INFORMS, vol. 18(2), pages 115-136.
    21. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    22. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    23. Natasha Zhang Foutz & Wolfgang Jank, 2010. "Research Note—Prerelease Demand Forecasting for Motion Pictures Using Functional Shape Analysis of Virtual Stock Markets," Marketing Science, INFORMS, vol. 29(3), pages 568-579, 05-06.
    24. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
    25. Carol J. Simon & Mary W. Sullivan, 1993. "The Measurement and Determinants of Brand Equity: A Financial Approach," Marketing Science, INFORMS, vol. 12(1), pages 28-52.
    26. Singfat Chu & Hean Keh, 2006. "Brand value creation: Analysis of the Interbrand-Business Week brand value rankings," Marketing Letters, Springer, vol. 17(4), pages 323-331, December.
    27. Tao Chen & Erin Pik Ki So & Liang Wu & Isabel Kit Ming Yan, 2015. "The 2007–2008 U.S. Recession: What Did The Real-Time Google Trends Data Tell The United States?," Contemporary Economic Policy, Western Economic Association International, vol. 33(2), pages 395-403, April.
    28. Nuno Barreira & Pedro Godinho & Paulo Melo, 2013. "Nowcasting unemployment rate and new car sales in south-western Europe with Google Trends," Netnomics, Springer, vol. 14(3), pages 129-165, November.
    29. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    30. De Vany, Arthur S. & Walls, W. David, 2004. "Motion picture profit, the stable Paretian hypothesis, and the curse of the superstar," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1035-1057, March.
    31. Andrew Ainslie & Xavier Drèze & Fred Zufryden, 2005. "Modeling Movie Life Cycles and Market Share," Marketing Science, INFORMS, vol. 24(3), pages 508-517, November.
    32. Zhang, Jason Q. & Craciun, Georgiana & Shin, Dongwoo, 2010. "When does electronic word-of-mouth matter? A study of consumer product reviews," Journal of Business Research, Elsevier, vol. 63(12), pages 1336-1341, December.
    33. Mark B. Houston & Ann-Kristin Kupfer & Thorsten Hennig-Thurau & Martin Spann, 2018. "Pre-release consumer buzz," Journal of the Academy of Marketing Science, Springer, vol. 46(2), pages 338-360, March.
    34. Chris Hand & Guy Judge, 2012. "Searching for the picture: forecasting UK cinema admissions using Google Trends data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1051-1055, July.
    35. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    36. Sigrid A Troelstra & Jizzo R Bosdriesz & Michiel R de Boer & Anton E Kunst, 2016. "Effect of Tobacco Control Policies on Information Seeking for Smoking Cessation in the Netherlands: A Google Trends Study," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-16, February.
    37. Yoo, Boonghee & Donthu, Naveen, 2001. "Developing and validating a multidimensional consumer-based brand equity scale," Journal of Business Research, Elsevier, vol. 52(1), pages 1-14, April.
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    2. Lash, Michael T. & Sajeesh, S. & Araz, Ozgur M., 2023. "Predicting mobility using limited data during early stages of a pandemic," Journal of Business Research, Elsevier, vol. 157(C).

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