Revealed Preference Theory is developed by Paul Samuelson (1938), a Nobel Prize winner, with the aim to identify the preferences of an individual (utility function) by observing actual choice behavior. The greatest advantage of RP data is that the respondents revealed the actual or observed choices made in authentic existence situations (Morikawa, 1989).
Therefore, revealed preference technique can avoid potential issues linked with hypothetical responses (McFaddden, 1974) which usually do not consider accurately about the choice behavior and strategic responses. In a decision making processing for utility maximizing, the agents often compare the alternatives among the others by estimating the benefits from observed actual risks (McFadden, 1974a). Additionally, Louviere et al. (2000) designated that revealed preference is an adequate method for small number of observations without any assumptions of functional forms.
Nevertheless, the application of revealed preference means that data merely observes what the choice was and the implication is that we cannot know surely what the choice set was. Consequently, Ben-Akiva and Lerman (1985) indicated that revealed preference survey collects less observation because of high cost. Moreover, the strong correlation between the attributes concerning the value of travel time, value of delivery time and travel cost, hence the effect’s estimation of independent variables on the utility maybe bias. The range of attributes is limited due to the dependence in the range of the actual decision made by particular consumption of existing products situations (Adamowicz et al., 1997), which may result in multicollinearity, estimation efficiency and endogeneity.
Moreover, Adamowicz et al. (1994) claimed that revealed preference can better estimate the use value and market value, while RP data with intangible attributes associated to reliability, convenience and comfort are difficult to measure by the researcher. Revealed preference survey cannot be applied to estimate the demand for new modes, since the information for new modes do not available or non-existing alternatives, instead stated preference survey would does (Morikawa, 1989, Ben-Akiva et al., 1994 and Louviere et al., 2000). Hence, the preference indicator is usually as choice (Morikawa, 1989).
Nevertheless, the application of revealed preference means that data merely observes what the choice was and the implication is that we cannot know surely what the choice set was. Consequently, Ben-Akiva and Lerman (1985) indicated that revealed preference survey collects less the observations because of high cost. Moreover, the strong correlation between the attributes concerning the value of travel time, value of delivery time and travel cost, hence the effect’s estimation of independent variables on the utility maybe bias. The range of attributes is limited due to the dependence in the range of the actual decision made by particular consumption of existing products situations (Adamowicz et al.,
1997), which may result in multicollinearity, estimation efficiency and endogeneity.
Moreover, Adamowicz et al. (1994) claimed that revealed preference can better estimate the use value and market value, while RP data with intangible attributes associated to reliability, convenience and comfort are difficult to measure by the researcher. Revealed preference survey cannot be applied to estimate the demand for new attributes, since the information for new attributes are not available or non does not exist, instead stated preference survey would do (Morikawa, 1989, Ben-Akiva et al., 1994 and Louviere et al., 2000).
The respondent’s shopping mode choices were conducted by a personal interview with the questionnaire to record the revealed responses in real-life. Following previous studies, we assume that different shopping modes give shoppers different utility levels, depending on their intrinsic attributes. This study elicits the utility function of shopping channel, online and in-store, from the choice decisions. The questionnaire is designed to collect respondent’s choices on choosing chopping modes from their book purchases in the last 12 months.
Firstly, to identify whether will the respondents be the right individual for this interview. Hence, respondents will be asked: “In the last 12 months, did you buy book in- store or online?”. If yes, they will be continue with “for the last purchase, are you the decision maker to buy?”. If they are the decision maker, they keep going on the interview.
Next, the general information of respondents as frequency of buying books will be displayed by asking: “How many books buying times have you bought in the last 12 months?” and “How far months ago have you bought to the last purchase?”. Then, the respondents were asked to reveal the past experience about book shopping and Internet use. The Internet use experience related to some questions like: “what is the most common device have you often accessed the Internet?” (in respect of mobile phone/ Laptop, PC/
Tablet/ Others one), and “how many hours per day have you accessed the Internet?”. To identify the categories of individuals, those who have ever bought books via online or not,
“Did you ever use the Internet for purchasing books”.
After all, to perceive the purpose of buying book of respondents, we asked them “the reason why you decided to buy the book?”. Finally, respondents were also required to answer the question about “Where did you buy the books in the last purchase?”, either
online channel or in-store channel. From that, respondents will be moved to the questions in terms of the last purchase of in-store shopping or online shopping.
If respondents made their shopping on in-store, they were also requested to respond to the questions about online shopping with associated attributes according to their knowledge (Hsiao, 2009), and vice versa. The attributes of revealed preference method were classified for the last purchase in both channels by these questions: “how much the total purchase price have you spent for the last book purchase?(VND)”. Moreover, we also asked:
“if you have bought via online (or in-store) for the last purchase, the total purchase price will be more expensive or cheaper? About how many percent?. These questions are to identify the purchase price for the last time.
The aim of collecting the information in value of time and cost that will be asked through these questions: “how many minutes have you spent for shopping time (ordering time)?”. Besides, to visibly ascertain the shopping time or ordering time that respondents have spent, we have: “Did you only buy books in the last purchase?”; “How many minutes have you spent for travel time in one trip? Or “how many days for delivery time?”. Besides that, the travel cost can be defined by “how much travel cost have you spent for the last purchase?”. In the case that respondents cannot estimate the value of travel cost, we can suggest that “what kind of transport have you used for the last purchase?” and “how far for distance to bookstore?”. Respectively, we have “how much delivery cost for the last purchase if you have bought via online?”.
Moreover, each respondent was asked to provide the personal information such as gender, age, marital status, education, occupation and income level. In addition, they were also asked to rate 18 attitudinal statements with five-point Likert scales. From that, this study can collect the database which is a foundation for stated preference data.
3.3. The stated preference data: methods of collection including