Hedonic analysis for consumer electronics using online product reviews

Chun Wen Li, Hui Chi Chuang, Sheng Tun Li

研究成果: Conference contribution

2 引文 (Scopus)

摘要

In recent years, online product reviews have been considered as a valuable source of information to assist people in making buying decisions. Most of prior studies on the effect of online product reviews have utilized the factors which manufactures cannot control by themselves, such as the number of reviews, the average review rating, as independent variables in their regression models. However, those factors cannot provide direct implications for manufacturers. For example, managers cannot easily increase the number of reviews to rise the product price or demand. In contrast, they have to trace the causes of why the amount of reviews grows. Thus, in order to offer more straightforward suggestions, we adopt the concept of hedonic analysis which decomposes the demand of a commodity into several product features to identify which of them impact its demand mostly. In this paper, we take smartphone market as our research target and propose a framework to demonstrate these assumptions. Our framework utilizes opinion mining techniques to extract sentiment words and features from online product reviews, and combines those extracted items with basic characteristics obtained from a specification of each product to form hedonic regressions. Finally, we provide managerial implications for firms and help them make proper strategies based on the experiment results.

原文English
主出版物標題Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
編輯Ayako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
發行者Institute of Electrical and Electronics Engineers Inc.
頁面609-614
頁數6
ISBN(電子)9781467389853
DOIs
出版狀態Published - 2016 八月 31
事件5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
持續時間: 2016 七月 102016 七月 14

出版系列

名字Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
國家Japan
城市Kumamoto
期間16-07-1016-07-14

指紋

Consumer electronics
Smartphones
Managers
Decision making
Specifications

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

引用此文

Li, C. W., Chuang, H. C., & Li, S. T. (2016). Hedonic analysis for consumer electronics using online product reviews. 於 A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (編輯), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (頁 609-614). [7557684] (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.171
Li, Chun Wen ; Chuang, Hui Chi ; Li, Sheng Tun. / Hedonic analysis for consumer electronics using online product reviews. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編輯 / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. 頁 609-614 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).
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Li, CW, Chuang, HC & Li, ST 2016, Hedonic analysis for consumer electronics using online product reviews. 於 A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (編輯), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557684, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., 頁 609-614, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16-07-10. https://doi.org/10.1109/IIAI-AAI.2016.171

Hedonic analysis for consumer electronics using online product reviews. / Li, Chun Wen; Chuang, Hui Chi; Li, Sheng Tun.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. 編輯 / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 609-614 7557684 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

研究成果: Conference contribution

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AU - Li, Sheng Tun

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Y1 - 2016/8/31

N2 - In recent years, online product reviews have been considered as a valuable source of information to assist people in making buying decisions. Most of prior studies on the effect of online product reviews have utilized the factors which manufactures cannot control by themselves, such as the number of reviews, the average review rating, as independent variables in their regression models. However, those factors cannot provide direct implications for manufacturers. For example, managers cannot easily increase the number of reviews to rise the product price or demand. In contrast, they have to trace the causes of why the amount of reviews grows. Thus, in order to offer more straightforward suggestions, we adopt the concept of hedonic analysis which decomposes the demand of a commodity into several product features to identify which of them impact its demand mostly. In this paper, we take smartphone market as our research target and propose a framework to demonstrate these assumptions. Our framework utilizes opinion mining techniques to extract sentiment words and features from online product reviews, and combines those extracted items with basic characteristics obtained from a specification of each product to form hedonic regressions. Finally, we provide managerial implications for firms and help them make proper strategies based on the experiment results.

AB - In recent years, online product reviews have been considered as a valuable source of information to assist people in making buying decisions. Most of prior studies on the effect of online product reviews have utilized the factors which manufactures cannot control by themselves, such as the number of reviews, the average review rating, as independent variables in their regression models. However, those factors cannot provide direct implications for manufacturers. For example, managers cannot easily increase the number of reviews to rise the product price or demand. In contrast, they have to trace the causes of why the amount of reviews grows. Thus, in order to offer more straightforward suggestions, we adopt the concept of hedonic analysis which decomposes the demand of a commodity into several product features to identify which of them impact its demand mostly. In this paper, we take smartphone market as our research target and propose a framework to demonstrate these assumptions. Our framework utilizes opinion mining techniques to extract sentiment words and features from online product reviews, and combines those extracted items with basic characteristics obtained from a specification of each product to form hedonic regressions. Finally, we provide managerial implications for firms and help them make proper strategies based on the experiment results.

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PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Li CW, Chuang HC, Li ST. Hedonic analysis for consumer electronics using online product reviews. 於 Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, 編輯, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 609-614. 7557684. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). https://doi.org/10.1109/IIAI-AAI.2016.171