Ouyou toukeigaku
Online ISSN : 1883-8081
Print ISSN : 0285-0370
ISSN-L : 0285-0370
Volume 44, Issue 3
Displaying 1-7 of 7 articles from this issue
Forum
  • Ataka Kazuto
    Article type: Forum
    2015 Volume 44 Issue 3 Pages 71-87
    Published: 2015
    Released on J-STAGE: June 29, 2016
    JOURNAL OPEN ACCESS
    With the arrivals of smartphones and broadband network, the world has changed dramatically in most fields, including communication, information search, logistics, and so on. The basic formula for fortune generations has also changed. Information communication technology sector (ICT) now plays the key role in economic growth, reflecting not simply profit, but also its critical influence for the future. Now three changes, emergence of the big data, explosive growth of computing capacity, and rapid evolution of data science, drive the advent of “Information Industrial Revolution (the 2nd Industrial Revolution)”, by which humans will be liberated from tedious number crunching and labor-intensive information handling. There are three key factors for an advanced economy to survive in this discontinuous change: (1) multi-big data availability across device and applications, (2) huge computational capacity, and (3) global-level ICT talents in both quality and quantity. However, Japan is hardly having leadership in any of these three, especially in talent supply. Only higher education system can develop those highly skilled talents on a large scale. Expectations for the academia cannot be any higher.
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Contributed Papers
Special Issue:
  • Takahiro Hosono, Tadahiko Sato
    2015 Volume 44 Issue 3 Pages 119
    Published: 2015
    Released on J-STAGE: June 29, 2016
    JOURNAL OPEN ACCESS
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  • Junichiro Niimi, Takahiro Hoshino
    Article type: Contributed Papers
    2015 Volume 44 Issue 3 Pages 121-143
    Published: 2015
    Released on J-STAGE: June 29, 2016
    JOURNAL OPEN ACCESS
    It is important for the companies to get much information about their customers. For example, the information that how often each customer purchases from the competitors can be useful when we plan the promotion strategy. In this paper, we focus on the web services and predict customers' behaviors such as browses and purchases in two companies and their competitors. We define “the variety” of User Access Patterns which has not received much attention in previous studies and show its usability to predict customers behaviors.
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  • Masataka Ban
    Article type: Contributed Papers
    2015 Volume 44 Issue 3 Pages 145-160
    Published: 2015
    Released on J-STAGE: June 29, 2016
    JOURNAL OPEN ACCESS
    In marketing literature, consumer behavior that is selecting one brand from various choices is called “brand choice”. For consumer heterogeneity, in general, brand choice behavior is modeled by hierarchical Bayes discrete choice model like a logit or probit which has consumer's individual-level parameters. This study proposes a brand choice model for consumer clustering in terms of a new product adoption. In particular, the model is constructed by hierarchical bayes probit model having a Dirichlet process (DP) prior with time ordering clustering constraint. Features of this model is that (1) the model enables the estimation of the number of clusters, and then it's not necessary to set that before analysis. (2) Time ordering clustering leads to estimation of breakpoints among consumer clusters.Consumer is categorized into an adequate time ordering cluster based on the similarity of market response.The model estimates provide useful information corresponding to the marketing concepts containing time ordering clusters like Rogers's innovation adoption curve, product life cycle management (PLC). The model is estimated by Markov Chain Monte Carlo sampling method, especially for the DP prior Neal (2000)'s Metropolis-Hastings based algorithm modified to fulfill the constraint is used.
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  • Kazuhiro Miyatsu, Tadahiko Sato
    Article type: Contributed Papers
    2015 Volume 44 Issue 3 Pages 161-182
    Published: 2015
    Released on J-STAGE: June 29, 2016
    JOURNAL OPEN ACCESS
    Our research aims to model mechanisms to describe consumer purchase quantity on a single shopping trip with consideration of consumer sentiment called mental accounting. The model consists of two components; (1) consumers mental loading that changes at each shopping trip, and (2) consumers purchase quantity generating mechanism, which switches regimes depending on degrees of the mental loading and threshold parameter individually estimated. The model is derived based on threshold Poisson regression model framework, and the total model parameters sets are estimated using Markov Chain Monte Carlo (MCMC) mehods. Empirical studies have been applied with ID-POS data from a retailer shop. Results indicate that our model outperforms by having consumer sentiment considered such as mental loading when they make purchase decisions. In addition, the model reveals individual payday as by-product through estimation of each mental loading conditions.
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