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Table 11.10 Variety and
level of an attribute
Attribute
Level 1
Level 2
Level 3
Store
Yes
No
-
Saturation
High
Low
-
Procedure
Easy
Difficult
-
Postage
Free
500 yen
-
Price
3,000 yen
4,000 yen
5,000 yen
e mV ni
j2C
P ni ¼
(11.3)
e mV nj
However, CL is the model achieved under two assumptions, homogeneous
preference and Independence from Irrelevant Alternatives : IIA . Although CL is
easy to analyze, it has the problem of weak model interpretability. Revelt and Train
( 1998 ) advocated the mixed logit model ML, which eliminates these two
assumptions. ML is a model that has a preference from which an individual differs.
When a certain respondent n chooses item i , utility is set to U ni , denoted as follows.
X
M
1 b
m
n x ni þ e ni
U ni ¼
V ni þ e ni ¼
(11.4)
e ni has an independent and identical type I extreme value
distribution, and the probability that the respondent n will choose i is formulized as
follows.
It is assumed that
ð Y
T
V i Þ
P j ¼ 1 exp
exp
ð
P ni ¼
f
ðbj O Þ
d
b
(11.5)
ð
V j Þ
t 1
T indicates the number of occurrences of the choice experiment, and several
repetitive questions are presented to the same respondent in the usual choice
experiment. f is the probability density function of
b
, and
O
indicates parameters
such as the average and variance of
.
In this study, the choice experiment was conducted using an orthogonal array
design; the variety of attributes and the level were set up as shown in Table 11.10 .
Eight profiles were created using the orthogonal array design from the level of
each attribute. Two profiles were combined at random, and a choice set with the
added option “Using neither online shop” was created. Each respondent answered
eight choice sets per questionnaire. An example of a choice set is shown in
Table 11.11 .
An alternative specific constant (ASC) was added to the analysis; ASC3 was
introduced into “Using neither online shop.” It can be interpreted as negative for the
purchase of goods from an online shop if the ASC is significantly estimated as
b
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