Using natural language analysis  of product reviews to detect features  contributing to life cycle price decline

Takayuki  Suzuki; Kiminori  Gemba

doi:10.23762/FSO_VOL10_NO1_4

Published : 2022-03-30

Vol. 10 No. 1 (2022)

Using natural language analysis of product reviews to detect features contributing to life cycle price decline

Takayuki Suzuki

https://orcid.org/0000-0001-6448-3601

Kiminori Gemba

https://orcid.org/0000-0002-0309-0764

DOI: https://doi.org/10.23762/FSO_VOL10_NO1_4

Abstract

As product life cycles become shorter, rates of price decline are accelerating. This study examined online reviews and used natural language processing and multiple regression analyses to identify the determinants that influence the rate of price decline in digital cameras. The coefficients for the external factors of portability, lens, and video were significantly positive, and the coefficients for battery, hold feel, underwater, and correction were significantly negative. Our proposed methodology can be employed for differentiation and product development strategies. Design, which has attracted attention in recent years in the context of value creation, did not affect the rate of price decline, suggesting that a full-scale re-examination of differentiation and product development strategies is imperative. This approach can be applied to product and marketing strategies.

Keywords:

hedonic approach, quantitative analysis, price rate, word-of-mouth communication, product strategy, natural language processing

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Suzuki, T. ., & Gemba, K. . (2022). Using natural language analysis of product reviews to detect features contributing to life cycle price decline. Forum Scientiae Oeconomia, 10(1), 68–80. https://doi.org/10.23762/FSO_VOL10_NO1_4

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Takayuki Suzuki

cm006070@ed.ritsumei.ac.jp
https://orcid.org/0000-0001-6448-3601

Affiliation:

Ritsumeikan University Japan

Bio Statement (e.g., department and rank)

Takayuki Suzuki is a software developer at a Japanese company. He received his master’s degree from the Graduate School of Technology Management at Ritsumeikan University. He received his Ph.D. from Ritsumeikan University, where his doctoral thesis was entitled “Information discovery methods that lead to innovation creation using natural language processing and statistical processing.” His research interests are web marketing and natural language analysis, among others.

Kiminori Gemba

gemba@hosei.ac.jp
https://orcid.org/0000-0002-0309-0764

Affiliation:

Hosei University Japan

Bio Statement (e.g., department and rank)

Kiminori Gemba is a professor at the Business School of Innovation Management at Hosei University. His research interests are innovation theories and strategic management. In 1992, he received a master’s degree in engineering from the University of Tokyo. He joined the research division of Sanwa Research Institute and his research focused on environmental management and new industry creation. He received his Ph.D. from the University of Tokyo in 1999. Subsequently, he became a research associate and an associate professor at the University of Tokyo and a professor at Ritsumeikan University.