The Impact of Tourism on the UK Economy
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The methodology of tourism management is generally predicated upon the same set of ontological and epistemological assumptions that is common to the methodology of social sciences as a whole. Thus, Dann, Nash, and Pearce (1988) differentiate between such aspects of social sciences methodology as the methodology per se (i.e., the set of procedures that are necessary for the purposes of establishing the logic of one’s research); specific investigation techniques (e.g., interviews, observations, ethnographies, etc.); and theory as such. The latter may be defined as a “body of logically interconnected propositions which provides an interpretative basis for understanding phenomena” (Dann, Nash, & Pearce 1988, p. 4). Accordingly, the discussion of this thesis’s methodological foundations will proceed within the framework of the aforementioned conceptualisation of the connections between various aspects of methodology so as to present a comprehensive outline of the relevant issues.
As suggested by Mason (2003), the impacts of tourism may be regarded as the results of the tourists’ continuous interaction with both human and natural environment of the respective destination area they find themselves in (Mason 2003, p. 28). Accordingly, three major types of tourism’s impact may be differentiated, with economic, socio-cultural, and environmental impacts showing on the top of the list of tourism’s consequences (Mason 2003, p. 27). Given the scope of the current inquiry, the discussion of the impact of tourism in the Brighton area will be limited to that of its economic aspects, while implementing relevant methodological approach.
Lickorish (1991) mentions such important economic impacts of tourism as contribution to foreign exchange earnings, contribution to government revenues, generation of employment, and contribution to regional development. As all of these variables are of properly quantifiable character, one should suppose that it is a quantitative research design that is to be utilised within this study’s context.
In the field of tourism studies nowadays, quantitative methods, mainly statistics-based ones, are routinely used to generate and validate findings pertaining to the specific research (Rosselló 2012, p. 31). For the majority of quantitative research designs, a correlation between relevant variables should be established with the help of regression analysis procedures (Rosselló 2012). In addition, the time series analysis should be undertaken so as to evaluate the appropriate changes in the variables selected for analysis and arrive at a larger ontological picture (Cang & Seetaram 2012; Frechtling 1996). The main purpose of the present study is to evaluate the impact of tourist activities upon the economic development of the Brighton region. Hence, a time series analysis would be the most useful here.
As a time series analysis would basically require the meticulous comparisons of the historical observations of the data in question, it would be less costly to implement other types of statistical analysis and testing (Song & Li 2008). Accordingly, a time series analysis and modelling will be undertaken in the course of this research, with the data stored and provided to the public by the Brighton local authorities, Tourism South East Research Council (2008), and other similar national and local bodies, which were collected and analysed in accordance with their statistical validity.
In the course of the research, such parameters of the time series analysis as the variables’ stationarity status (with the augmented Dickey-Fuller (ADF) test being used for its determination; Dickey & Fuller 1979), seasonality, structural breaks, and others were taken into account (Cang & Seetaram 2012). A regression analysis will be carried out upon the results at hand in order to determine whether the changes in the variables listed by Lickorish (1991) would significantly correlate with changes in tourists’ numbers for the period between 1975 and 2012. Thus, the key patterns of tourism’s economic impact upon the Brighton area may be convincingly established.
In order to implement the aforementioned model, the use of certain specific methods of both primary data analysis and model building would be required. Proceeding from the major features of the research design outlined above, the use of factor analysis as the main data analysis method and of simple regression as the key model building technique may be found to be instrumental for the purposes of this research.
Following Baggio and Klobas (2011), factor analysis may be characterized as a means of searching for the recognizable sets of quantitative data that are closely correlated with some of their peer sets in the general data set but are weakly correlated with the others (Baggio & Klobas 2011, p. 43). In the context of this study, the use of factor analysis would enable the researcher to extract and evaluate the main factors of the data pertaining to the time series fluctuations with respect to the changes in both tourist visit numbers and the respective economic variables. Following the delineation of factors, the regression analysis would be undertaken so as to establish plausible correlation between the data connected with the economic impact variables and the changes in tourists’ inflows.
Structural Equation Modelling (SEM) as the Study’s Theoretical Model
While regression analysis may be a useful and simple tool with respect to establishing relevant correlations between the variables under consideration, some of its limitations would require the use of a more comprehensive model to account for the variables’ interconnectedness. Nunkoo and Ramkissoon (2012) suggest that a Structural Equation Modelling (SEM)-based model of multivariate regression analysis may be used to fulfil such an objective. Referring to such important studies in tourism management as the ones conducted by Schmidt, Cantallops, and dos Santos (2008), or Wober and Gretzel (2000), we argue that the SEM model, with its emphasis upon the concurrent measurement of both observed and unobserved variables, could be used in a variety of tourism studies. In the most general terms, the SEM may be characterized as the statistical technique aimed at specifying, estimating, and evaluating the “models of linear relationships among a set of observed variables in terms of a generally smaller number of unobserved variables” (Shah & Goldstein 2006, p. 149). Accordingly, the usage of the SEM model in the context of this study would require the definition and integration of the possible latent variables (LVs) that may be implemented if such conditions as linearity of all relationships, normality of data, and the regular data measurement on ratio or interval scale may be satisfied (Reisinger & Movondo 2007). Hence, the testing of limits of such assumptions would be a major part of the research process.
Proceeding from the discussion above, one may come to the conclusion that the evaluation of the impact of tourism upon Brighton’s economy, as a representative case of this very impact upon the UK economy at large, would proceed in accordance with the orthodox quantitative research design, with such instruments as factor analysis and simple regression being successively tested with the use of the SEM statistical model. Hence, the analysis and interpretation of the primary data available to the researcher would conform to the standards of scientific rigor and enable one to proffer statistically valid predictions, based upon the properly tested quantitative techniques. This would enhance this study’s validity and prepare the grounds for subsequent inquiries in the similar research directions, as well as prevent any possible errors in the course of analysis.