Understanding of Tourism Visitation - Research Proposal Example

The profile and satisfaction of visitors that booked tour operation services through the VIC for the 2008/2009 summer period A RESEARCH PROPOSAL Project Methodology The study will include: Step1: Identifying sample to be involved in the research through a sampling technique of simple random sampling out of the entire population of VIC’s visitors’ base for the year 2008/2009. Step 2: Data collection using the questionnaire through face-face interviews. Step 3: Data input into SPSS and data analysis using the already mentioned descriptive and analytical statistical techniques. Step 4: Interpretation of the results obtained according to the parameters and statistical tables and thumbs rules that exist Step 5: Representation of the interpreted results in line with the relevance to the project aim and objective undertaken to be fulfilled. Research Design The study would be carried out by distributing survey questionnaires to the visitors of VIC in the summers of 2008/2009 (after their consent). The questionnaire responses would be anonymous in nature and could not be traced back to individual respondents. Afer this collection of data is done, an analytical and empirical data analysis would be conducted to interpret the results of the survey. Pilot Study In order to verify and re-confirm the research survey and hypothesis, a pilot study would be conducted. Results of the pilot study will authenticate whether the survey is clear, suitable and appropriate (Zikmund, 2003). Moreover, results will show that the research problem, objectives and hypothesis are fine. Data collection The purpose of the current research is to investigate the various areas of research so as to contribute to understanding and knowledge of tourism visitation to the destination and the operations and management of the VIC (visitor information centre). This will further help in exploring the profile and satisfaction of visitors that booked tour operation services through the VIC for the 2008/2009 summer period. For collecting data relating to this issue, a questionnaire will be developed and distributed to a simple random sample of VIC customers. Questionnaire is selected because it is very cost effective when compared to face-to-face interviews (Aaker, Kumar & Day, 2007). Moreover, it is easy to analyze and it is familiar to most people. Not only questionnaire reduces bias, but it also less intrusive than telephone or face-to-face surveys (Ashram). The questionnaire will be developed using a mix of both categorical scales like nominal and ordinal scales as well as continuous scales like interval scales. Responses to questions of the questionnaire will be taken on 5-point scales ranging from (1) Strongly agree through (5) strongly disagree. Data Collection Tool The questionnaire to be prepared would be in a brief and concise format with direct questions. The use of leading questions and very seeking very personal data such as income and social status shall be avoided. The scales to be used to prepare the questionnaire would be nominal, ordinal as well as interval scales where the nominal and ordinal scales would be coded using numerals such as for instance: Gender (Nominal Scale) Male = 1 and Female = 2 Years of Association with VIC (Ordinal Scale) Less than 1 month = 1 3 to 4 months = 2 6 to 7 months = 3 9 to 10 years = 4 More than 1 year = 5 Age (Ordinal Scale) Below 18 yrs = 1 19 – 30 yrs = 2 31 – 45 yrs = 3 46 – 60 yrs = 4 Above 60 yrs = 5 The interval scales would be a 5-point Likert scales ranging from strongly agree to strongly disagree. These scales would be used to decode the responses collected through the questionnaire and enter it in an excel sheet. The excel sheet format consisting of numbers representing various response is essential as the SPSS software statistical analysis employed in this research required numerical data inputs. Data Collection Technique/ Sample Selection Simple random sampling technique would be used to carry out the survey. The samples would be collected from amongst the visitors of VIC. Thus, the questionnaire would be designed such that only those who have availed the services of VIC i.e. those who are VIC’s visitors would only take part in the survey. The research has been confined to the visitors of VIC for the 2008/2009 summer and hence has a region or area specific results and corresponding significance. Information Sources The data would be collected in a face to face interview with a set of customers identified through the random sampling technique as mentionedabove.. Face to face interviews is considered appropriate as it gives the researcher a chance to explain the research objective and clarify doubts and apprehensions regarding any of the questions in the questionnaire then and there. However, these interviews are also very time consuming and require a lot of fieldwork. Participants The participants of this study will include the visitors that booked tour operation services through the VIC for the 2008/2009 summer period. A simple random sample of these visitors will be created. The size of the sample will depend on the size of the population of the actual number of visitors to the VIC during the summers of 2008/2009. . Reliability and Validity Factor analysis would be carried out to test the validity of the research measures and the Cronbach’s alpha test would be a measure of reliability of the scales. A detailed description of both these is included along with the description of the Data Analysis later in this report. Ethical Considerations Considering the nature and motivation behind the research being the commercial interests, the ethical considerations abound this are likely to arise. It is appropriate to expect that the results of the research and survey conducted would be utilised towards achieving betterment in the business propositions of VIC through aiding their understanding of the present experience of their customers. However, the researcher and all the parties associated with this research are ethically liable to protect the personal information of the respondents to the survey. It must be ensured that no selling of such information to outside bodies and neither the use of same to encroach upon the privacy of VIC’s customers who have participated in the survey, would take place. Data Analysis Data will be analyzed by using the suitable descriptive and analytical statistical methods of SPSS package. The statistical methods that will be used in the analyses are: 1) Descriptive statistics such as frequencies, percentages, mean, median, mode, standard deviation, variance etc. to describe the variables and measures. 2) Factor analyses to test the validity of the measures. 3) Correlation analysis especially using Pearson correlation to assess the relationships between the various scale items. 4) Reliability analysis using Cronbach’s alphas to test for the reliability of the scales Principles or theory involved with the data analysis The data collected is a mixture of personal demographic inputs like age, gender and association with VIC along with inputs as specific responses towards attitude measuring scales. As mentioned already in this report, certain statistical procedures would be used to carryout the analysis of data collected. A brief understanding of the theoretical background behind these principles has been explained below: (a) Descriptive statistics: These are generally used to summarize and describe the collected data for exploration purposes which helps in making general inferences about the distributions of the data (Coakes, S. J., 2005, pp.56). Categorical scales such as nominal and ordinal ones are appropriately depicted through bar charts and continuous scales like interval and ratio are better depicted through histograms. Frequencies illustrate the magnitude of occurrence of a measured variable and the respective percentages. (b) Factor Analysis: This technique basically categorizes large volumes of variables into manageable smaller sets of factors which describe fully all the individual variables taken up for assessment originally. Factor analysis also acts as means to determine if the variables measure the same construct or not. (c) Correlation Analysis: A linear relationship between two or more variables is identified through this statistical procedure where Pearson-correlation analysis is done to establish the extent of relationships between continuously scaled variables like interval scale data. A particular result at a significance level which pertains to the prescribed thumb rule value tells whether the two variables are highly correlated i.e. measure same or similar constructs or not. (d) Reliability Analysis: This statistical procedure tests the reliability of scales through a measurement of reliability coefficient ‘Cronbach’s alpha’ ranging from 0 to 1. Average correlation of items is used for analysis if items are standardized; otherwise average covariance of items is used. Analysis Procedure Data analysis would be done using SPSS software employing various statistical measures to validate the scales used as well as interpret the usability of the data collected to assess the relationship between different variables and their impact on VIC. Various descriptive statistical processes like frequencies and explore; bivariate statistics like correlation and some other analytical statistical processes like factor analysis, reliability analysis were employed. The steps followed for the statistical analysis has been summarized below (Manning & Munro 2007, p. 40): (a) Descriptive analysis: Frequency tables along with other descriptive statistics can be obtained by entering input data in SPSS data editor and then clicking on Analyze, descriptive statistics, frequencies which opens the dialog box where variables are selected along with other required options such as charts, percentages, etc. Figure 1: Descriptive Statistics (b) Factor Analysis: This analysis involves steps such as correlation matrix computation, Factor extraction and then rotation. A principal component factor analysis can be conducting in SPSS by clicking on Analyze, data reduction, factor to open the dialog box where the variable are input and the various tabs given are used to select preferences for descriptive, extraction, rotation, etc (Field, 2005). Figure 2: Factor Analysis (c) Correlation Analysis: Correlation has certain underlying assumptions such as the data are collected from related pairs with linear relation between two variables, interval or ratio scaled data, normally distributed scores of variable, etc. In SPSS, click analyze, correlate, bivariate to obtain the bivariate correlations dialog box where ‘Pearson-correlation’ option is selected while we have done analysis for this research. Figure 3: Correlation Analysis (d) Reliability Analysis: Reliabilities which can be of various types such as Cronbach’s alpha (Cronbach, 1951), Split half reliability, Guttman, Parallel and Strictly Parallel; but in this research, an alpha reliability analysis has been carried out by clicking analyze, scales, reliability scales and selecting alpha in the dialog box that opens (Peter J., 1979). Figure 4: Reliability Analysis Research Evaluation The method of data analysis chosen as illustrated above is appropriate to the assessment undertaken in this research. A need to identify variables which impact the buying profile and satisfaction levels of VIC’s customers and the further classification of these several variables into broad identifiable and understandable concepts requires the analysis to proceed with a large number of variables initially and then carry out a data reduction statistical technique which enables these variables which are identified by the researcher to be categorized on the basis of the correlation existing amongst them and the amount of variance explained by each of these. Thus, it is appropriate to carry out a factor analysis for the data collected in this research. Secondly, identification of the personal demographic pattern on a macro level of the population availing VIC’s information to destination regions along with accommodation and local tour operation booking services needs an analysis done which would effectively summarize and describe these data into identifiable pattern. Thus, the need to apply the descriptive statistical techniques here which could present the demographic pattern as well as continuous interval scaled data in terms of the frequency and percentage of occurrence. This analysis gives a general idea and information of the overall sample taking the survey. Another feature of this analysis is the output in the form of visual graphics such as graphs and charts which aid a better understanding of the data understudy. Categorization of the variables identified is achieved through factor analysis, however, even within these categories, how do the different variables interrelate and affect each other can be obtained through a bivariate correlation analysis. The person correlation coefficients indicate the extent to which the variables depend on each other and hence, provide insight into patterns existing between these variables which result in a quality analysis and the impactful results. The questionnaire consisting of the data to be researched upon is prepared on the basis of certain assumptions and parameters which are reflected in the scales being used. These scales need to checked and tested for their homogeneity which indicates internal consistency, validity of the construct of these scales as well as reliability test of these scales. These tests are carried out using statistical processes like PCA (Principal component analysis), Cronbach’s alpha etc. The PCA is conducted as we do the factor analysis, where the various measures like communalities data, total variance data and rotated factor matrix leads to the testing of validity and internal consistency of the scales being used in the research questionnaire. For reliability tests, evaluation of the value of Cronbach’s alpha gives a fair idea towards the reliability score of the scales used. Read More