Abstract
In this chapter we have attempted to use “Relative to an Identified Distribution” (RIDIT) algorithms based modelling for analysing real-time empirical data relating to tourists' attitude and preference for a better understanding of the tourists' motivation and behaviour. RIDIT approach for evaluating the factors that influence tourist behaviour is not a very common approach in tourism sector. This chapter on modelling tourists' opinions and perceptions with RIDIT analysis would try to guide the empirical research in the domains of hospitality, tourism and travel research and analytics process in generating Optimized research outcomes.
TopIntroduction
Travel and tourism is considered as one of the largest and fastest growing industries (Ninemeier & Perdue, 2008; Cooper & Hall, 2008) and it is also strongly influencing the world GDP, employment, exports and taxes (Kay, 2003; Koc, 2004; Ninemeier & Perdue, 2008). World Travel & Tourism industry has contributed US$7. 6 trillion (9.8% of global GDP) and created 277 million jobs (1 in 11 jobs) directly or indirectly in 2014 (UNWTO, 2015). According to the latest UNWTO World Tourism Barometer, it has been expressed that international tourists (overnight visitors) reached 1,138 million in 2014 with the growth rate of 4.7%, which is 51 million more than in 2013. Tourism is not only a social phenomenon it is also a big business (Cohen, 1979; Krippendorf 1986). Hence, growing popularity of global tourism demands proper destination marketing (Echtner & Ritchie, 1993; Murphy et al., 2007). To achieve this, tourism marketers nowadays need to apply modern branding techniques and effective positioning strategies (Hosany et al., 2006; Skinner, 2008; Hankinson, 2009; Hanna & Rowley, 2011) for motivating the prospective tourists and that it would also be helpful to cope up with current unstable and changing competitive tourism marketing environment (Ekinci & Hosany, 2006; Pike et al., 2004). Pike (2009) established that 70% of worldwide travellers visit only 10 countries and the remaining countries are struggling to attract the outstanding 30% of the total international arrivals (Morgan et al., 2003). This is not just the problem of destination marketing but also the problem of understanding the tourists’ insights and preferences. Not only in the product market but also in the tourism sector tastes and preferences vary. Tourists are not completely the similar in nature; they have different set of needs, motivations and preferences relating to their ideal vacation. Tourists are heterogeneous in nature (Dolnicar, 2008), so it is very essential to project the different tourists’ destination brands with proper tourism marketing initiatives to attract the different prospective tourist segments (Echtner & Ritchie, 1993; Bhat & Reddy, 1998; Hankinson, 2005; Mowle & Merrilees, 2005; Wood, 2007; Campo-Martinez et al., 2010). In this context the judgement of tourists’ preferences and attitudes (George, 2003; March & Woodside, 2005) are necessary to achieve the equilibrium in tourism marketing. In better understanding the travel behaviour, it is important to know how the key attributes and factors relating to destination brand influence tourist choices (March & Woodside, 2005; Holloway, 2004). Various researchers have argued that tourist motivations analysis helps in better understanding of travel behaviour (Huang & Xiao, 2000; Lee, Lee & Wicks, 2004; Law, Cheung & Lo, 2004; Correia et al., 2006; Saayman, et al., 2009). Hence travel motivation and behaviour are considered as the important fields in tourism marketing research literature (Huang & Xiao, 2000; Lam & Hsu, 2006). The prediction of travel motivation and behaviour play a significant role in tourism marketing domain, in order to generate more demand and also this to help in tourists marketing decisions (Holloway, 2004; March & Woodside, 2005; Decrop, 2006; Smallman et al., 2010). “In order to understand tourists’ behaviour, researchers have considered the primary data relating to tourists’ assertiveness, attitudes, interest perceptions, behavioural changes, intentions, and knowledge. These types of tourists’ data and information are frequently measured and gathered by using Likert scale (as cited by Wu, 2007 in Likert, 1932; Fink, 1995; Fink et al., 1998; Peterson, 2000; Alexandrov, 2010; Boone et al., 2012).” Instead of commonly used statistical approaches for Likert scale data examining, we have used RIDIT algorithms based modelling in analysing the tourists’ opinions and preferences.
Key Terms in this Chapter
Travel Motivation: Travel motivation is the inner state of a person, or certain needs and wants of the tourists that can be considered as one of the most important psychological influences of tourist behaviour.
Likert Scale: A Likert scale is a psychometric scale commonly involved in research and it is the most widely used approach to scaling responses in survey research, such that the term is often used interchangeably with rating scale, or more accurately the Likert-type scale, even though the two are not synonymous.
Travel Behaviour: This refers to the way in which tourists behave according to their attitudes before, during and after travelling.
Tourists: The World Tourism Organization defines Tourists as people who “travel to and stay in places outside their usual environment for more than twenty-four (24) hours and not more than one consecutive year for leisure, business and other purposes not related to the exercise of an activity remunerated from within the place visited”.
RIDIT Analysis: RIDIT (Relative to an Identified Distribution) is a very efficient technique that can be used to examine the Likert scale data. The outcomes from the RIDIT analysis can be used to arrange Likert scale items either in an ascending or in a descending order based on importance.
Tourism: The World Tourism Organization defines tourism as “Tourism is a social, cultural and economic phenomenon which entails the movement of people to countries or places outside their usual environment for personal or business/professional purposes. These people are called visitors (which may be either tourists or excursionists; residents or non-residents) and tourism has to do with their activities, some of which imply tourism expenditure”.
Kruskal-Wallis Statistics W: Kruskal-Wallis statistics W is a hypothesis testing tool which follows the ? 2 distribution with (m – 1) degrees of freedom. Here ‘m’ is the total number of variables.