(1927) in the seminal on psychophysical discrimination with explaining dominance judgments among pairs of alternatives (Adamowicz et al., 1998). This law postulated that consumers when facing choice among mutually exclusive alternatives would choose the alternative � that has the best stimulus level ��. The stimulus comprises of two components,
the systematic and random components, or �� = �� + ��. When the perceived stimulus is
interpreted as utility, the theory became an economic theory (McFadden, 2001). Marschak (1960) has developed a theoretical framework for the choice probabilities of utility maximization that included random components, which is called Random Utility Maximization (RUM) (McFadden, 2001).
According to McFadden (2001), utility function is specified as �(�), where � is thevector of consumed quantities of various goods. If the consumer prefers a bundle of goods
1 1 2 2 2 1 2
�1 = {�1 , … ,
��} to �
= {�1 , … , ��}, then �(�
) >
�(�
). The utility maximization subject to a budget constraint �� ≤ �, where � is consumer’s income and � is the vector of
price with demand function � = � ,(((((((((((((( ( �). The general problem was established as
following
���{�1,…,��} �(�1, … , ��) S.t. ∑� ���� ≤ �
If we know the utility function and income of every consumer, the problem will be resolved for each consumer. From that, we will find out the total market demand for many consumers and � = {�1, … , ��} is the consumer demand from good 1 to good n.
In discrete choice framework, ��� is the utility that agent of choice (decision maker, person, firm) � gets from a set of bundle options (alternatives). The utility- maximizing is that a consumer � has to face with a choice set � which composes 1, … , �� numbers of
alternatives. Each consumer must choose only one alternative from a choice set. The choice of the consumer is indicated by a binary variable, ���, for each alternative:
��� =
The discrete model can be operationalized by specifying the utility function, usually in a linear form where the utility of consumer � from alternative �
��� = ���� + ���
Ben-Akiva and Lerman (1985) supposed that attributes determine the utility and these attributes are characteristics of each alternative. In consumer choice behavior of choosing shopping mode, there are many attributes that determine the utility of shopping channel choice. Nonetheless, the most prominent attributes are purchase price, time and cost (Bateman et al., 2002).
In studies of store choice, purchase price probably plays an important role in making decision. Purchase price in online stores are typically lower than traditional stores. This may be because those selling online can save the overheads in terms of reducing the cost of hiring sales staff and renting space. However, the online sellers have to charge the shipping cost, in addition to the selling price of commodities. The final price is probably lower than the price of the same item outside the store (Hsiao, 2009). Furthermore, a major consumers subsist on urban areas tend to search information related to commodities online before they travel to store (Farag et al., 2007). The aim of this activity is to compare the sell price of the same good between online and in-store channel, and gather other necessary information. As Grewal et al. (2003) revealed that the asymmetric information of selling price in traditional
�=1
1 �� ��� > ���
{0 ��
stores and the possibilities to make up the price difference of marketers can be significantly eliminated, which thanks to the transparency of price information on the Internet. Besides, Koyuncu and Bhattacharya (2004) concluded that since the online shopping channel offers better prices, the consumers therefore prefer to buy more via the Internet.
Travel is another attribute that creates the differences between online and traditional in-store shopping (Hsiao, 2009). The consumers must spend travel time and travel cost in order to conduct a shopping trip to store. In the meantime, if the consumers choose to shop online, they need to spend time for ordering, time waiting for the delivery of products and may be pay for shipping fee. Truong and Hensher (1985) pointed out that disutility of individuals was caused by travel time. In other words, Hsiao (2009) stated that if the consumers waste their travel time and travel cost, these things will have direct effects on their values, and therefore leading to the diminishing utility of consumers. In this case, shopping online appears to be a better choice for consumers.
If the consumer’s purchase online, after finalizing the payment transaction for the product’s value, they have to wait for product delivery. Only when the products are music (mp3 file), software or certain kinds of services like Internet banking and online consultation, then no delivery time is needed. However, the satisfaction of products will be decreased if the products are delayed in terms of delivery. In addition, an uncertainty about the products will be created, because it will be associated with product’s quality which the consumers often predict (Liu and Wei, 2003). Moreover, Koyuncu and Bhattacharya (2004) found that delivery time is was one of the reasons that has direct effect on the choice of consumers, if longer delivery time would cause less purchase to buy over the Internet from consumers.
Besides the main effects of these attributes, the individual characteristics and attitudinal forward shopping channels play an important role. According to Adamowicz et al.
(1998), the choices can vary systematically from individual to individual, and to compute for this differences as much as possible, the set of explanatory variables can be extended to include individual differences such as demographic and psychological factors, �, with respect to the vector of coefficients �. These individual difference measures may be hypothesized to influence utility levels via intercept and/or slope coefficients in the � vector.
Based on RUM, the customers make the decision to choose either online shopping or in-store shopping whichever generates the higher level of utility. The two utility functions for in-store and online shopping are functions of attributes:
��� = ����� + �1������� + �2����������� +
�3�������������� + �4�ℎ����������� (1)
��� = �1������� + �2�ℎ������������ + �3������������ +
�4������������ (2)
This is the Basic model. The main attributes utilized in this study is adopted from Hsiao (2009) and Schmid et al. (2016).
In the equation (1), ��� is the indirect utility function of online shopping channel, which covers the key variables as
• ���������������� �� is the alternative specific constant of online shopping.
• ������� : The effect of purchase price of online shopping on online shopping
utility.
• �����������: The effect of ordering time on online shopping utility.
• ��������������: The effect of delivery time on online shopping utility.
• �ℎ�����������: The effect of shipping fee (or delivery cost) on online
shopping utility.
In the equation (2), ��� is the indirect utility function of in-store shopping channel, which contains the key variables as
• �������: The effect of in-store purchase price on utility of in-store shopping.
• �ℎ������������: The effect of shopping time on in-store shopping utility.
• ������������: The effect of travel time (to store) on in-store shopping utility.
• ������������: The effect of travel cost on in-store shopping utility.
The Full model in this study is estimated with attributes interacted with individual characteristics:
• Online ASC (������������������) is interacted with dummy variable for office worker, and internet access frequency (hours/day). This is to allow for difference in online
20
shopping among office workers and non-office workers, and among those with different internet access frequency.
20
• Prices of both alternatives (online and in-store) are interated with income.
This is to allow for different price sensitivity among shoppers with different income.
• Delivery time is interacted with gender to allow for female shoppers may be more patient in waiting for the delivery.
• Travel time (to store) is interacted with income, as shoppers with higher income may have higher opportunity cost of time.
The utility function of the Full model is then:
��� = ����� + �1 ì ����� ì ������ + �2 ì ����� ì
���������� + �1�������
+ �3 ì ������� ì ������ + �2����������� +
�3�������������� + �4
ì �������������� ì ������ + �4�ℎ����������� (3)
��� = �1������� + �1 ì ������� ì ������ + �2�ℎ������������
+ �3������������ + �2
ì ������������ ì ������ + �4������������ (4) By doing so, the full model is able to explore more obvious comprehension about how the consumers decide to choose the specific-alternative utility maximization. In addition to the main attributes presented in the basic model, the full model complements the following variables, in the equation (3) and (4), respectively as
• ���������������� �� ì ������: The additional effect of the online shoppers who are office
workers and non-office workers on utility of online shopping.
• ������������������ ì ����������: The additional effect of the online shoppers who have
the internet access frequency on utility of online shopping.
32
• ������� ì ������: People who have the different levels of income with
respect to the sensitivity of online purchase price, which have the additional effect on utility of online shopping.
• �������������� ì ������: People who are female or male with respect to the sensitivity of delivery time, which have the additional effect on utility of
online shopping.
33
• ������� ì ������: People who have the different levels of income with
respect to the sensitivity of in-store purchase price, which have the additional effect on utility of online shopping.
• ������������ ì ������: People who have the different levels of income with respect to the sensitivity of travel time, which have the additional effect on
utility of online shopping.
From the estimated coefficients, we can calculate the willingness to pay (WTP) of time and cost attributes. WTPs can be calculated from the Basic model as follows
� 2
��������� �1
� 3
��� ��� ������ �1 (7)
� 2
�ℎ���� ����� � �1
� 3
���������� �1 (8)
We use the �1 (coefficient of price in the in-store utility function) as the marginal utility of money instead of �1 (the online shopping utility function), as people have to pay immediately when shopping in-store while they may pay later when shopping online and as a result, �1 may not really reflect the marginal utility of instant money. Equation (5) shows the WTP of one minute spend for searching and placing the order over the internet (VND/min). Equation (6) exhibits the WTP of one minute spend for finding and buying the items of product at the store (VND/min). Equation (7) shows the WTP of one day waiting for the delivery of purchased products (VND/day). Equation (8) exhibits the WTP of one minute spend for travelling to a store (VND/min).
Table 3. 1: The store choice attributes description
Variable Unit Description Expected sign
������� VND 1000 Purchase price of online shopping:
the price when buying online.
����������� Minute Ordering time: the time spends for searching and placing the order of books over the internet.
�������������� Days Delivery time: the day of waiting for the delivery of purchased books
Negative (-)
Negative (-)
Negative (-)
��� = (5) ��� =
��� = (6) ��� =
(within 1 day, 2-3 days and >= 3 days).
�ℎ�����������VND 1000 Shipping fee (or delivery cost): the monetary value which pays for the delivery of purchased books (VND 0, VND 15 and VND 22).
������� VND 1000 Purchase price of in-store shopping:
The purchase price at the bookstore.
�ℎ������������Minute Shopping time: the time spent for finding and buying books at the bookstore.
������������ Minute Travel time: the time spends to go to the bookstore.
������������ VND 1000 Travel cost: the monetary value which depends on the reported transportation and the distance to the store for the last purchase in the questionnaire.
Negative (-)
Negative (-)
Negative (-)
Negative (-)
Negative (-) Calcul ating the WTPs using the Full model is more
complicated.
In the Full model, the marginal utility of money is �1 + �1�̅̅�̅�̅̅�̅̅�̅̅
�̅, where
�̅̅�̅�̅̅�̅̅�̅̅�̅
is an average income. The WTPs for these attributes is now
�2
�3+�4ì�̅̅̅�
�̅̅�̅
����
������
�� = −
� +�
�̅̅�̅̅�̅�̅̅�̅̅̅�̅
(9)����
�� ���������������� �
�����
= −
� +
+ + + + + + + + + + + + + +
�̅̅�̅̅�̅�̅̅�̅̅̅�̅
(10)
1
1 1
�2 1 �� ̅ ̅ ̅ � ̅ ̅ � ̅ 3 ̅ � ̅ �+� ̅ ̅ � ̅ 2
����ℎ���� ���������������� � �� = −
� +++++++++++++++ �̅̅�̅̅�̅�̅̅�̅̅̅�̅ (11)
������������� = −
� +� �̅̅�̅̅�̅�̅̅�̅̅̅�̅ (12)
1 1
1
1
In the same manner, the WTPs of these attributes in equation (9), (10), (11) and (12) are also described as in the Basic model.
However, the marginal utility of delivery time is
�3 + �4 ì �̅̅�̅̅�̅̅�̅̅�̅�̅, where ������ is an average.
The choice of shopping mode � ∈ {��, � } follows the utility maximization for respondent � is modeled as
� ���, � > ���,�:
�ℎ����� = {
������ �ℎ������
�
�ℎ������ � − �����
�ℎ������
Therefore, the probability of choosing shopping channel is calculated as
���,�
�
(
�
ℎ
�
�
�
�
�
,
�
|
�
�
,
�
)
=
∑
�
�
�
�
,
The definitions and units of�
measurement as well as the expected sign of the
alternative-specific attributes and social- demographic variables used in this study are described in Table 3.1 and Table 3.2, respectively.
This study use both Revealed Preference (RP) data and Stated Preference (SP). The data collection used for this paper is organized in two stages. Stage I contains the questionnaires based on RP method to obtain the revealed data of the respondents.
Stage II comprises the chocie experiment, also known as SP method which was developed from stage I. By doing so, we can get the stated choice from the respondents. According to Louviere et al.
(2000), revealed preference method collect the shopping mode data in “the world as it is”, meanwhile stated preference method identified “the world as it could be” to infer the shopping channel choice behavior. In other words, RP survey points out that what the respondents actually did, while, SP survey expresses what the respondents would do in the hypothetical scenarios.
Table 3.
2: Individual characteristics variable description
Variable Unit
Description
Gender 0 = Male;
1 = Female
Occupation 1
=
�
s t u d e n t s
;
2
=
h o u s e w i f e
;
3
=
g o v e
rn m en t o ffi ce rs;
4
= fr ee la nc er s;
5
= co m pa ny e m pl oy ee s;
6
= re se ar
c h e r s
;
7
=
u n e m p l o y m e n t
;
8
=
r e t i
Monthly income In million
VND
1 = Under 5; 2 = 5 – 10; 3 = 11 – 15; 4 = 16 – 20; 5
= 21 – 25; 6 = 26 – 30; 7 = 31 – 35; 8 = 36 – 40; 9 =
41 – 45; 10 = 46 – 50 and 11 = Over 50.
Internet access frequency
In hours 1 = 2 – under 4; 2 = 4 – under 6; 3 = 6 – under 8; 4
= 8 – under 10 and 5 = Over 10.
Figure 3.1 describes the conceptual framework of the choice between online shopping versus in-store shopping.
Each method of Revealed and Stated Preference has their own advantages and disadvantages, so, in order to understand why the researches often incorporate both approaches, we first examine shortly about the two methods in the way how to collect research data.
Online ASC Online ASC*Office workers
Purchase price Purchase price
Online ASC* Internet access frequency
Shopping time Delivery time
Purchase price
*Income Purchase price
*Income Travel time
Ordering time
Travel time
*Income Shipping fee Delivery time *Gender Travel cost
Online shopping attributes In-store shopping attributes
Utility: Online vs.
in-store shopping
Choice of shopping mode
Figure 3.1: Choice model between online and in-store shopping