We estimate a mixed logit demand model for the Japanese digital content market and measure willingness to pay for service attributes. We find that the willingness to pay for an extra unit of service quality ranges from 62 Yen to 83 Yen per month. Meanwhile, the switching cost, a measure of disutility from switching to other service providers is estimated as 340 Yen ($3) per month, which is approximately the same as the current monthly subscription fee.Willingness to Pay, Digital Contents

Donghun Kim

Philip Sugai

English

Research Papers in Economics

     
 
 
  
  
Volume 30, Issue 3 
  
Willingness to Pay for Digital Contents in Japan 
  
 
 
Donghun Kim  
Yonsei University 
Philip Sugai  
International University of Japan
Abstract 
We estimate a mixed logit demand model for the Japanese digital content market and measure willingness to pay for 
service attributes. We find that the willingness to pay for an extra unit of service quality ranges from 62 Yen to 83 Yen 
per month. Meanwhile, the switching cost, a measure of disutility from switching to other service providers is 
estimated as 340 Yen ($3) per month, which is approximately the same as the current monthly subscription fee.
 
Citation: Donghun Kim and Philip Sugai, (2010) ''Willingness to Pay for Digital Contents in Japan'', Economics Bulletin, Vol. 30 no.3 pp. 
1745-1752. 
Submitted: Jan 27 2010.   Published: July 12, 2010. 
 
       1 
 
                                                                                                             
I. Introduction 
 
In this paper we estimate a mixed logit demand for the digital content in the Japanese PC 
and mobile markets and measure consumer’s willingness to pay for service attributes. We 
first estimate a demand function of digital contents using a hierarchical Bayesian model 
(Allenby, 1997, Train 2001) and analyze how the service attributes affect the consumers’ 
utility.  We  then  use  the  estimated  parameters  in  the  demand  function  to  measure 
consumers’ willingness to pay for each service attribute. In particular we are interested in 
the measurement of willingness to pay for switching to other service providers. Usually, 
switching behavior is influenced by switching cost, which is defined as any perceived 
disutility a consumer would experience from switching service providers (e.g., Klemperer, 
1987). 
  We  find  that  the  service  attributes  have  significant  effects  on  the  consumers’ 
utility and their preference for the service attributes is distributed with a large variance 
across the consumers. We also find that willingness to pay for more robust content was 
83  Yen  per  month  and  that  for  content  update  frequency  was  62  Yen  per  month. 
Meanwhile, the overall consumer switching cost was 340 Yen (about US$3) per month, 
which is approximately the same as the current content subscription fee of 300 yen per 
month.  We  also  find  that  the  switching  is  highly  correlated  with  service  quality,  in 
particular, service robustness, provided by other service providers.  
 
 
II. Market and Data 
 
 
We collected data using a choice-based conjoint (CBC) survey to examine consumers’ 
preferences regarding digital content and their loyalty to the providers of such content. In 
the analysis, each surveyed consumer was presented with several hypothetical offers by 
content service providers and was asked to identify which offer the consumer  would 
choose.  Additionally, for each set of hypothetical offers, respondents were also given the 
choice to reject all offers provided.  
For  this  research,  respondents  were  recruited  from  mobile  and  PC  content 
subscribers. The paper specifically focused on two of Japan’s most powerful consumer 
brands offering content via PC and mobile channels, Disney and Yahoo!. According to 
the  2005  Nikkei  Brand  Japan  survey,  these  two  brands  were  the  two  highest-ranked 
Internet and service companies in Japan. In this study, in total n=825 Disney or Yahoo! 
respondents randomly recruited from Macromill Inc.’s 1.8 million member subscriber 
panel. On this panel, 45% (n=372) of respondents were female and the other 55% were 
male. The average age of respondents was 33 years with a standard deviation of 8.9 years. 
In total, all 47 prefectures and wards across Japan were represented. The survey was filed 
in March 2006, using an Internet-based survey interface.  
Respondents  answered  demographic  questions  about  themselves  along  with  a 
total of 15 conjoint analysis choice experiments. In each experiment, respondents were 
asked  to  select  from  five  conjoint  cards  that  contain  different  brands  with  different   2 
 
attributes. The number of choice experiments was designed to provide a wide variation in 
choices  for  each  attribute.  Because  Yahoo!  And  Disney  competitors  are  distinctly 
different, separate competitors were chosen for Disney and Yahoo! Subscribers in the 
conjoint experiments. For Disney subscribers, competing brands included in the conjoint 
experiments were Bandai Networks, Studio Ghibli, Nana, and Doraemon. For Yahoo! 
Subscribers, competing brands included in the conjoint experiments consisted of the top 
five  competitive  Japanese  web  portals  to  Yahoo!Japan  in  terms  of  overall  traffic, 
including Google, Rakuten, Infoseek, MSN and goo. All these portal sites were available 
both via the PC and the mobile phone. 
The price attribute represented the monthly subscription fee for digital content, 
and had six levels which increased in 100 Yen/month increments, ranging from level 1, 
“free” (or 0 Yen/month) to level 6 “500 Yen/month”. The content robustness attribute 
represented  the richness of the content provided, and had five levels, ranging from level 
1“much less robust content than my current content provider.” to level 5 “much more 
robust content than my current content provider”  Finally, the content update attribute 
represented the frequency at which new content is added to an existing digital content 
service.  This attribute consisted of six levels ranging from level 1, “content is updated 
less than once a month” to level 6 “content is updated every day.”  
 
 
III. The Model 
 
We estimate consumer utility from content use with a mixed logit model, which provides 
a flexible specification for representing the distribution of preferences in the population 
and the choices of each customer (Bhat, 1999; Brownstone and Train, 1999; Train, 1998;  
Lee et.al, 2006). 
Consider a choice among alternatives J=1, 2,…, J in choice situations t=1, 2,…, T.  
Then the utility that consumer i obtains from choosing choice j in situation t is 
 
' njt n njt njt U x b e = + (1), 
where  njt e  is  the  iid  extreme  value  error  term  and  n b  denotes  the  value  that  each 
consumer places on the attributes and is normally distributed with mean b and variance W, 
i.e.,  ~ ( , ) n N b W b . n b  varies randomly over consumers, reflecting the range of consumer 
preferences  regarding  service  providers’  attributes. njt x  represents  a  vector  of  choice 
characteristics that include price and the dummy for switching, content robustness, and 
content update. The switching dummy is equal to 1 if a consumer chooses an alternative 
to her present service provider and 0 otherwise. We can then rewrite the utility function 
as follows. 
 
1 2 2 4 njt n njt n n njt n njt njt U Price Switching ContentRobustness ContentUpdate b b b b e = + × + × + × +
(2) 
   3 
 
where nk b is the consumer n’s coefficients for attribute k. We estimate the mixed logit 
model using the hierarchical Bayesian method (Allenby 1997, Train 2001, Train et al. 
2003).  
Suppose that a study has priors on b and W. The prior on b is assumed to be 
normal  with  an  unboundedly  large  variance  while  the  prior  on  W  is  assumed  to  be 
inverted  Wishart  with  K  degrees  of  freedom  and  scale  matrix  I,  the  K-dimensional 
identity  matrix.  Let’s  denote  consumer  n’s  chosen  alternative  through  all  time  as
1 2 ' ( , ,..., ) n n n nT y y y y = .  The  chosen  alternatives  across  the  entire  sample  are  then 
represented  as 1 2 ( , ,..., ) T Y y y y = .  Then  the  conditional  on n b ,  i.e.,  the  probability  of 
consumer n’s observed choices, is 
 
exp( ' )
( | , )
exp( ' )
nt
T ny t
n n
t njt j
x
L y x
x
b
b
b
= P
å
           (3) 
 
The unconditional probability is the integral of  ( | , ) n L y b W  over all values for b : 
                       
( | , ) ( | ) ( | , ) n n L y b W L y b W d b f b b =ò       (4), 
 
where  ( | , ) b W f b is the normal density with mean b and variance W.  ( | , ) n L y b W is the  
 
mixed logit probability. The posterior distribution of b and W is 
 
                                            ( , | ) ( | , ) ( , ) n
n
K b W Y L y b W k b W µ Õ         (5), 
 
where  ( , ) k b W is the prior on b and W, i.e., normal for b times inverted Wishart of W. Our 
goal is to obtain information on the population distribution of preferences associated with 
each attribute of service providers. For this purpose, we take draws from the posterior 
distribution to get information about the posterior. We use Gibbs sampling to take draws 
where  draws  are  taken  sequentially  from  the  conditional  posterior  of  each  parameter 
given  the  previous  draw  of  the  other  parameters.  The  sequential  of  draws  from  the 
conditional posteriors converges to draws from the joint posterior. 
We assume that the parameters in the utility function are distributed normally. In 
many  applications,  however,  such  an  assumption  may  be  inappropriate.  A  normal 
distribution  for  a  price  coefficient  implies  that  some  consumers  prefer  higher  prices. 
Willingness to pay for an attribute can be unboundedly high for consumers whose price 
coefficients are near zero, because willingness to pay is the coefficient of the attribute 
divided by the price coefficient (Train et. al, 2003). In this paper, we therefore transform 
the  price  coefficient  to  lognormal  distribution  such  that  the  price  coefficients  for  all 
consumers have the same sign. In estimation, the negative of price data is entered such 
that the coefficient on price is positive for all consumers. We can therefore interpret the 
positive coefficient as the preference for price reduction. 
   4 
 
Table1. Estimation of Population Parameters 
 
   
 
Mean of b  
 
 
Variance of b  
Price  -0.3764 
(0.0472) 
1.0568 
(0.0987) 
Switching  -1.2590 
(0.0818) 
2.3754 
(0.2340) 
Content Robustness  0.3064 
(0.0217) 
0.1508 
(0.0155) 
Content Update  0.2276 
(0.0184) 
0.0997 
(0.0107) 
       Log likelihood  -7122.63 
                            Note: The numbers in parentheses are standard errors 
 
 
IV. Results 
 
Table  1  represents  the  estimated  parameters  in  the  mixed  logit  model.  These  are  the 
means of the 1000 draws of b and of the diagonal elements of W. From the classical 
perspective,  these  are  the  estimated  population  means  and  variance  of n b .  All  the 
estimates are significant. At the mean, switching has a negative effect on utility. Content 
robustness and content updates have positive effects on utility. In particular, the highly 
significant variances indicate that parameters do indeed vary in the population. This also 
suggests that the mixed logit model is more appropriate than a logit model that imposes 
the same coefficients for attributes across consumers. The mixed logit technique allows 
each  coefficient  to  have  a  mean  and  variance  in  the  population,  while  standard  logit 
contains fixed coefficients, which is equivalent to a mixed logit with zero variances. 
                            
Table 2. Mean and Variance of Coefficients with  
                                         Transformation of normal 
   
Mean 
 
Variance 
Price  1.1522 
 
2.6851 
Switching  -1.2671 
 
2.4493 
Content Robustness  0.2990  0.1616 
Content Update  0.2284  0.0973 
 
 
   5 
 
Table 2 presents the mean and variance of coefficients. Two thousands draws of 
n b  were taken from a normal distribution with mean equal to the estimated value of b 
and variance equal to W. Each draw of  n b  was then transformed to obtain a draw of 
coefficients as shown in Table 2. Table 3 shows the distribution of population for each 
coefficient. For 78.6% of the population, switching to other service providers produces a 
negative effect on utility. A similar proportion of the population, 78.3%, prefers content 
robustness while the rest of the population dislikes it. Content update produces positive 
utility for 76.5 percent of population, which means that it produces negative utility for the 
remaining 23.5 percent. 
 
                           Table 3. Share of population for coefficients with  
                                         Transformation of normal . 
 
Coefficient  Shares  strictly  below  zero 
and at zero(%) 
Price  0.0 
Switching  78.6 
Content Robustness  21.7 
Content Update  23.5 
 
 
Table 4 presents correlations among coefficients. It indicates that switching is 
positively correlated with content robustness and content update. This suggests that even 
though switching to another provider is costly, consumers are willing to switch insofar as 
other  brands  provider  better  content  robustness  and  content  update.  Switching  is 
positively correlated with reduction in price. If other service providers charge high prices, 
consumers  will  not  switch.  The  results  also  show  that  switching  is  correlated  more 
strongly with content robustness than with other attributes. 
 
                Table 4 Correlations among coefficients with transformation of normal 
                 
   
Price  Switching 
 
Content 
Robustness 
 
Content 
Update 
Price  1.00  0.120  0.108  0.008 
Switching    1.00  0.361  0.222 
Content Robustness     1  1  0.272 
Content Update        1 
 
As Table 2 indicates, 78.6% of the population suffers utility loss from switching 
to other service providers. Therefore, without compensation, consumers will not switch. 
How much, then, should they be compensated? Table 5 shows the estimated consumers’   6 
 
willingness  to  pay  for  each  attribute.  The  willingness  to  pay  for  each  attribute  is 
calculated as the marginal utility of each attribute,  /
njt njt U x ¶ ¶ , divided by the negative of 
the derivative of utility with respect to price, i.e., the price coefficient. Willingness to pay 
for switching providers, i.e., the switching cost, is negative 340 Yen (US$3) per month. 
This  amount  is  approximately  the  same  as  the  current  monthly  subscription  fee  for 
mobile  digital  content  of  300  yen  per  month.  Meanwhile,  for  content  robustness  and 
content update, consumers are willing to pay 83 Yen and 62 Yen a month more for each 
unit of increase, respectively. 
 
Table 5 Willingness-to-Pay (Unit: 100 Yen/month/unit) 
 
   
Mean 
 
Median 
 
Variance 
Switching  -3.40  -3.36  0.26 
Content Robustness  0.83  0.82  0.02 
Content Update  0.62  0.61  0.01 
 
 
V. Conclusion. 
 
 
In this paper we estimate consumers’ utility function for the Japanese mobile and PC 
content market and analyzed how service attributes affected the consumers’ utilities. We 
find that the consumers are willing to pay a large monthly fee, which is approximately the 
same as the current monthly subscription fee not to switch to other service providers and 
that  consumers’  switching  is  largely  affected  by  the  service  attributes  provided  by 
competitors. The results imply that high quality service makes it possible for service 
providers to charge high monthly fees and to prevent consumer switching.       7 
 
References 
 
Allenby, G., 1997, “An Introduction to Hierachical Bayesian Modeling,” Tutorial Notes, 
Advanced Research Techniques Forum, American Marketing Association. 
 
Bhat,  C.,  1999,  “Quasi-Random  Maximum  Simulated  Likelihood  Estimation  of  the 
Mixed Multinomial Logit Model,” Transportation Research, part B 35, 677-693. 
 
Brownstone, D., and K. Train, 1999, “Forecasting New Product Penetration with Flexible 
Substitution Patterns,” Journal of Econometrics 89, No.1, 109-129. 
 
Klemperer, P., 1987, “Markets with Consumer Switching Costs,” Quartely Journal of 
Economics 102, 375-394. 
 
Lee,  Jongsu,  Yeonbae  Kim,  Jeong-Dong  Lee,  and  Yuri  Park,  2006,  “Estimating  the 
Extent  of  Potential  Competition  in  the  Korean  Mobile  Telecommunications  Market: 
Switching  Costs  and  Number  Portability,”  International  Journal  of  Industrial 
Organization 24, 107-124. 
 
Train K., 1998, “Recreation Demand Models with Taste Differences Over People,” Land 
Economics 74, No.2, 230-239. 
 
Train  K.,  2001,  “A  Comparison  of  Hierarchical  Bayes  and  Maximum  Simulated 
Likelihood for Mixed Logit,” Department of Economics, Working Paper, University of 
California at Berkely. 
 
Train, Kenneth and Carrettt Sonnier, 2003, “Mixed Logit with Bounded Distributions of 
Parthworths,” Department of Economics, University of California, Berkeley. 

Willingness to Pay for Digital Contents in Japan

Abstract

Similar works

Full text

Available Versions

Research Papers in Economics