Page 1 Page 2 距離と内属の存在論ためには「私が光景に意味を与える

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音楽購買における新しい消費者行動モデルのシステムデザイン :
ベイジアンネットワークを用いた有償購買確率向上モデル
安田, 照(Yasuda, Akira)
当麻, 哲哉(Toma, Tetsuya)
慶應義塾大学大学院システムデザイン・マネジメント研究科
修士論文 (2013. 3)
Thesis or Dissertation
http://koara.lib.keio.ac.jp/xoonips/modules/xoonips/detail.php?koara_id=KO40002001-00002012
-0057
2013
3
1
Master's Dissertation
2012
Designing of Novel Consumer Behavior
Model of Music Purchase
-The Model for Enhancing Purchase Probability by
Bayesian Network -
Akira Yasuda
(Student ID Number : 81133614)
Supervisor Tetsuya Toma
March 2013
Graduate School of System Design and Management, Keio University
Major in System Design and Management
2
81133614
1998
IT
Shapiro &
Varian, 1999
Robert
Ioanna(2011)
420
400
20
Greedy Search
5
SUMMARY OF MASTER’S DISSERTATION
Student
Identification
Number
81133614
Name
Akira Yasuda
Title
Designing of Novel Consumer Behavior Model of Music Purchase
-The Model for Enhancing Purchase Probability by Bayesian Network Abstract
The music software market in Japan has been decreasing since 1998 mainly due to the illegal music
download. As a background, music today has become free downloadable information goods rather than
purchased material goods due to digitization of sound sources and diffusion of broadband internet
(Shapiro & Varian, 1999). On the other hand, some consumer is still willing to purchase music with
specific intentions. We highlighted Japanese music consumer behavior of purchasing music software
rather than acquiring it through internet without payment. The purpose of the research is to analyze
the purchase intentions of Japanese music consumers using Bayesian network modeling so as to
understand the essential causes for the payment. First of all, we conducted the multiple interviews with
Japanese music consumers and identified the five major purchase intentions: 1) appreciation intention,
2) collection intention, 3) value-added intention, 4) support intention, and 5) communication intention.
Our hypothesis is that “Music” influences to “Appreciation intention”, “Looks” and “Humanity”
influence to “Collection intention” and “Value-added intention”, and “Activity” influence to “Support
intention” and “Communication intention”. The scrutiny of these purchase intentions is the originality
of the research. Robert and Ioanna (2011) analyzed both legal and illegal acquisition of music software
but they did not consider the purchase intentions of consumers and therefore their model could not
answer why consumer tempted to pay for the music even when they could acquire it without payment.
Second, we conducted an online questionnaire on a sample of 420 people. We asked their "purchase
method", "purchase intention", "attitude", "confidence", "awareness of illegal downloading", "music
purchase amount", and "consumers attribute". Third, we build the Bayesian network model using 400
samples and validated the model with the other 20 samples. The structure of the model was defined by
structural learning method using greedy search algorithms. The results showed that different purchase
intentions be made from the differences of consumer’s attitude and confidence. Among the five purchase
intentions, appreciation intention was influenced by every confidence and attitude factors and therefore
would be a common driver for every purchase. Collection intention was influenced by “looks” and
“activity”, value-added intention was influenced by “humanity”, “live shows”, and “animation”, and
support intention was influenced by “background” and “plan”. This means that we are able to estimate
consumer’s purchase method on the basis of their attitude and confidence on music. Finally, we
examined estimation accuracy of the model concerning each consumer’s music purchase behavior. We
compared the model estimation results and the answers from the marketing managers of music
industry in Japan. The result showed that the model could provide the right answer with around 60% of
probability and the managers with only around 20% of probability.
Key Word(5 words)
Music purchase, Consumer behavior model, Bayesian Network, Information goods, Purchase
intention.
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65
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