Page 316 - 360.revista de Alta Velocidad - Nº 6

Kim, Junghwa. Schmöcker, Jan-Dirk. Li, Yeun-Touh. Demizu, Fumiaki.

Numerous models having been developed to assess the role of WOM in the diffusion process
(Dodson and Muller, 1978; Mahajan et al., 1990; Mazzarol, 2011). This was first noted by Brooks
(1957) as the influence on consumer purchase decisions and the role of opinion leaders in
purchasing behavior. Engle et al. (1969) described early adopter of new product and service
usually provide positive WOM and this is used to be a combination of media. Therefore a positive
relationship between WOM and advertising has been established in marketing literatures
according to Day (1971) and Lampert and Rosenberg (1975). Mazzarol (2011) described that the
diffusion of innovation is a social process in which interpersonal communication plays a key role.
In some transport studies, this diffusion of innovation has been considered. Costa and Fernandes
(2012) identified the diffusion of urban public transport modes i.e. trams and trolleybuses, as
well as organization of public transport markets across European cities. Jensen et al. (2016)
predicted the potential demand for electric vehicles through combining disaggregate choice
models and diffusion models based on the assumption that innovation penetrates the market for
new product or technology over time through a diffusion process. Wei et al. (2009) conducted
a comparative analysis on the traditional vehicles and EV by the forecast model based on
diffusion theory which was developed to identify the market expansion of EV in different fields.
One might expand the application range though further to daily travel behaviour. Abou-Zeid et
al. (2013) noted that the “informational mass effects” mentioned by Schmöcker et al. (2014) and
the “interaction effect in loose social networks” proposed by Ben-Akiva et al. (2012) are related
to WOM. These studies describe the effect of WOM on transport behaviour such as illegal parking,
unauthorized crossing, mode choice and attitudes. Belgiawan et al (2016) try to quantify social
network effects for mode preferences. Abou-Zeid et al. (2013) discuss further resulting examples
of social psychological marketing and public effects for transport management.
Related to the methodology chosen in the following, Parkes et al. (2013) presents an analysis of
the recent increase in the number of public bike sharing systems in Europe and North America
with the data examined through the lens of diffusion theory. In this study we consider HSR
demand under the assumption that the individual usage of high-speed railway might be affected
by social interaction. We suggest the results could be the basis to predict the demand patterns
of a new railway system in the future as well as to manage HSR sustainably.
3. Methodology and Assumption

Bass’s diffusion theory has been considered as a good starting point for modeling the long-term
penetration pattern of new technologies (Jensen et al., 2016; Lilien et al., 2000). Bass (1969)
observed that market absorption of new products or technology can be explained through a
model with two groups and then suggested the behavioral theory that an innovative product/
technology is usually adopted first by a few people, “innovators”, who in turn influence others,
“imitators” to adopt it. The innovators can be hailed as a small population group who can adopt
new technology as soon as the product is on the market. Then imitators follow by also adopting
these slowly after some time until market satisfaction is reached. Generally this leads to an
S-shaped curve that describes the diffusion of a new product/technology as shown in Figure 3.

Fig. 3 S-curve (Cumulative curve) (Left) and Density function (Right) in Bass Diffusion Model

314 360.revista de alta velocidad