Modeling the customer behavior in the mobile payment on a non-connected vending machine platform

In Zahir Azami, et al. (2004) the authors have presented a solution for mobile payment on a non-connected vending machine platform, with two methods: one manual and the other automatic (with infrared). The solution is a hybrid system, i.e., it accepts these two payment methods as well as currency (principally coins). The first observation is that users will use the system with any of the three methods and we can not deterministically find when and how users will pay for their purchases on the vending machines. However, the consumer behavior is very important for answering several questions. For instance knowing the percentage of total transactions by a mobile allows us to validate the business case on the system. Knowing the percentage and the distribution of the mobile transactions per total transactions allows us to engineer an optimal route management system. It also permits to more accurately design for capacity in the back-end server. However, there are several parameters that can significantly impact the consumer behavior, as people have different priorities. Some of the main parameters are the type of vending machine or sales outlet (cold beverage, snack, food, hot beverage, parking meter, public transport, etc); the location of the sales outlet and the demography of the location (university campus, manufacturing area, business offices, public places, etc); whether or not there is a surcharge for the mobile transactions and how much it is; the time of the day/day of the week season, etc. It is assumed that for the coin transaction, there is no inventory data transmission, for manual mode there is a small amount of information and for the automatic mode much more data can be transmitted. So the inventory information in the back-end server is sampled with a variable sampling frequency and a variable bit rate. This paper addresses the modeling for such a complex customer behavior, by applying simulation and comparing the results to real data obtained from field trials.