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The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference

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  • Jun, Seung-Pyo
  • Park, Do-Hyung
  • Yeom, Jaeho

Abstract

In recent years, many researchers have devoted their attention to using search traffic information gathered from Google Insights to carry out consumer attitude research. The purpose of this study is to assess the effectiveness of using search traffic information to analyze actual consumer attitudes regarding a product. By comparing the results of conventional survey-based attitude research with the results of search traffic information, this study reveals that search traffic information indicates consumers' level of interest regarding a product, the product attributes that they are considering, and the importance of each attribute to them. Also, it demonstrates the potential benefits of search traffic analysis, which can be useful for forecasting consumer preferences regarding products. Focusing on the Prius, a hybrid car, this study shows that search traffic information serves as an accurate indicator of consumer attitudes, and even succeeds in identifying consumers' hidden attitudes toward the Prius, which can be explained by cognitive dissonance theory. Finally, this study utilizes search traffic information to forecast changes in consumer attitudes and to develop an econometric model of consumer demand for the Prius by incorporating environmental variables such as the WTI (West Texas Intermediate) price. This study concludes that search traffic information offers new potential advantages, in that it not only overcomes the limitations imposed by the high cost of conducting surveys, in terms of money and time, but also helps to reduce the distortions caused by conscious or unconscious errors committed by survey respondents.

Suggested Citation

  • Jun, Seung-Pyo & Park, Do-Hyung & Yeom, Jaeho, 2014. "The possibility of using search traffic information to explore consumer product attitudes and forecast consumer preference," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 237-253.
    • Handle: RePEc:eee:tefoso:v:86:y:2014:i:c:p:237-253
    DOI: 10.1016/j.techfore.2013.10.021
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    1. Seung-Pyo Jun, 2012. "An empirical study of users’ hype cycle based on search traffic: the case study on hybrid cars," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 81-99, April.
    2. Jun, Seung-Pyo, 2012. "A comparative study of hype cycles among actors within the socio-technical system: With a focus on the case study of hybrid cars," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1413-1430.
    3. 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.
    4. Kwak, So-Yoon & Yoo, Seung-Hoon, 2012. "Ex-ante evaluation of the consumers' preference for the 4th generation mobile communications service," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1312-1318.
    5. Azar, Jose, 2009. "Electric Cars and Oil Prices," MPRA Paper 15538, University Library of Munich, Germany.
    6. Beatty, Sharon E & Smith, Scott M, 1987. "External Search Effort: An Investigation across Several Product Categories," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(1), pages 83-95, June.
    7. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    8. 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.
    9. David Haugh & Annabelle Mourougane & Olivier Chatal, 2010. "The Automobile Industry in and Beyond the Crisis," OECD Economics Department Working Papers 745, OECD Publishing.
    10. Jun, Seung-Pyo & Yeom, Jaeho & Son, Jong-Ku, 2014. "A study of the method using search traffic to analyze new technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 82-95.
    11. Ozaki, Ritsuko & Sevastyanova, Katerina, 2011. "Going hybrid: An analysis of consumer purchase motivations," Energy Policy, Elsevier, vol. 39(5), pages 2217-2227, May.
    12. Bettman, James R & Park, C Whan, 1980. "Effects of Prior Knowledge and Experience and Phase of the Choice Process on Consumer Decision Processes: A Protocol Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 7(3), pages 234-248, December.
    13. Chen, Peng-Ting & Hsieh, Hsin-Pei, 2012. "Personalized mobile advertising: Its key attributes, trends, and social impact," Technological Forecasting and Social Change, Elsevier, vol. 79(3), pages 543-557.
    14. 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.
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    6. Jun, Seung-Pyo & Sung, Tae-Eung & Park, Hyun-Woo, 2017. "Forecasting by analogy using the web search traffic," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 37-51.
    7. Jun, Seung-Pyo & Park, Do-Hyung, 2016. "Consumer information search behavior and purchasing decisions: Empirical evidence from Korea," Technological Forecasting and Social Change, Elsevier, vol. 107(C), pages 97-111.
    8. Hulya Bakirtas & Vildan Gulpinar Demirci, 2022. "Can Google Trends data provide information on consumer’s perception regarding hotel brands?," Information Technology & Tourism, Springer, vol. 24(1), pages 57-83, March.
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