A random extraction method with high market representation for online surveys

Takumi Kato, Noriko Kishida, Takahiko Umeyama, Yuexian Jin and Kazuhiko Tsuda

International Journal of Business Innovation and Research, 2020, vol. 22, issue 4, 569-584

Abstract: Due to their superior pricing and collection speed when compared to other survey methods, there is significant demand for online surveys in market research. However, online surveys have been reported as being biased. The problem we recognise in this research is that no method to improve accuracy in online surveys has been proposed, even though many types of research on bias have been reported. There are three hypothetical requirements for improving precision: 1) being able to cover the entire population; 2) being able to conduct random sampling; 3) being able to obtain responses without incentives. As a result of examination for the Chinese market, it became clear that the new investigation method satisfying the hypothesis is more accurate than the traditional online panel survey.

Keywords: marketing research; online survey; market representativeness; random extraction; random domain intercept technology; RDIT; China; automotive market; cultural map. (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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