Marketing Mix Factors Study Result analysis of PCA Result analysis of PCA

The principal components factor method was used to generate the initial solution. The eigenvalues suggested that a eight-factor solution explained 75.42% of the overall variance before the rotation. But as the researcher used partial components analysis, seven-factors were determined before the computation, which together explained 71.31% of the overall variation. The seven factors are the marketing mix factors of the tourism industry, 7Ps:

Product, price, place, promotion, people, process and physical evidence.

In the table „Total Variance Explained’ included in APPENDIX II, eigenvalues are listed in the first column of the first block. Eigen values measure the amount of variation in the total sample accounted for by each factor and they determine which components remain in the analysis. Components with an eigenvalues of less than 1 account for less variance than for the original variable, and so are of little use. Since variables are standardized to have means of 0 and variances of 1, total variance is equal to the number of variables; the second column in the table listed the measure of amount of variance in relation to total variance. The column cumulative percentage of variance contains the cumulative percentage of variance accounted for by the current and all preceding principal components. For example, the third row shows the value of 47.608 which means that the first three components together account for 47.608% of the total variance. Extraction sum of squared loadings in the second block of the table are the same for PCA extraction. Eigenvalues after extraction will be lower than their initial counterparts.

Components Matrix: The loadings are measure of overall perception about the effectiveness by using each of the marketing mix factors, the matrix is given in APPENDIX II:

The first Principal Component (PC) itself expresses 29.20% of variation in the data which corresponds to the highest value of the variable „route maps‟ (0.794) and the lowest value corresponds to „connecting tours‟ (0.108). The route map is supposed to be the most contributing variable among the variables in the PC1. On the contrary, connecting tours are not very contributing for the satisfaction of the visitors in Lumbini. So, its barely effective

for the prospective visitors. The second most contributing factor is „safety and hygine‟ as well as „physical evidence‟ with coefficient 0.760 and the third most contributing one is „ruls

& regulations‟ with coefficient 0.736.

Similarly, for the second PC, 10.16% of variation of the data set, the highest value is observed for the factor „exhibitions and events‟ (0.676) and the lowest corresponds to the factor „ticketing‟ (-0.437).

The third PC shows 8.24% variation in the data set and its most contributed by the factor

„shopping‟ (0.445) and less contributed by the factor „provision of exchange and insurance‟

(-0.567).

From the observation of the table, it can be said that tourism stakeholders in Lumbini contribute more for the first component (all of the factors are positive with high values). The researcher considered “Product” as the first component for the marketing mix study. So the stakeholders in Lumbini should focus their great effort to make the destination Lumbini a better product in future as well. They should focus on the factors such as safety and hygiene, route maps, rules, events, advertising & publicity, recreation, segmented product/prices as observed in each of the seven components. Similarly, visitors have very less satisfaction for the factors such as connection of tours, ticketing, provision of exchange and insurance, hospitality, travel agents and operators, tour packages for different components.

The KMO and Bartlett’s test shows two tests that indicate the suitability of data for structure detection. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.582 is a statistic that indicates that proportion of variance in variables that is caused by underlying factors. Value greater than 0.5 indicates that factor analysis is useful with the data. The overall significance of the correlation matrix was 0.000, with a Bartlett test of sphericity value of 3.546. The statistical probability and the test indicated that there was a significant correlation between the variables, and the use of factor analysis was appropriate.

Multiple Regression Analysis

The multiple regression analysis tool is used to measure the dependent variable „tourists‟

overall level of satisfaction‟ with the seven independent variables. In other words, the relation between overall satisfaction and marketing mix factors is measured.

The model summary table reports the strength of the relationship between the model and the dependent variable. R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R Square, the coefficient of determination, is the squared value of the multiple correlation coefficients. It shows that about half the variation in time is explained by the model.As a further measure of the strength of the model fit, compare the standard error of the estimate in the model summary table to the standard deviation of time reported in the descriptive statistics table.

The ANOVA table tests the acceptability of the model from a statistical perspective. The regression row displays information about the variation accounted for the model. Similarly, the residual row displays information about the variation that is not accounted for by the model. While the ANOVA table is a useful test of the model‟s ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship.

The multiple regression tables show the results of the regression analysis. To predict the goodness-of-fit of the regression model, the multiple correlation coefficient (R), coefficient of determination (R square), and F ratio were examined. First, the R of independent variables on the dependent variable (tourists‟ overall level of satisfaction) is 0.884, which showed that the tourists had positive and high overall satisfaction levels with the given attributes or marketing mix factors. Second, the R square is 0.781, explaining that more than 78% of the variation of tourists‟ overall satisfaction was explained by those factors. Last, the F ratio, which explained whether the results of the regression model could have occurred by chance, had a value of 21.50 (p=0.00) and was considered significant. The regression model achieved a satisfactory level of goodness-of-fit in predicting the variance of tourists‟ overall satisfaction in relation to the seven factors, as measured by the above-mentioned R, R square

and F ratio. In other words, at least one of the seven factors was important in contributing to tourists‟ overall level of satisfaction with Lumbini destination.

While interpreting the coefficient table included in APPENDIX II, coefficients of regression analysis are taken to the consideration. In the unstandardized coefficients block, the Y-intercept (constant) is 1.511 which can be interpreted as the value that would be predicted for Overall Satisfaction (Y) if all of the attributes‟ value correspond to 0. The result showed that the Overall satisfaction can be expected an average scale of 1.511 in Lumbini with 0 values for other attributes. Beta for X1 (attractiveness) represents the difference in the predicted value of Y for each one-unit difference in X1, if other Xn remain constant. This means, if X1 differed by one unit and Xn did not differ, Y will differ by B1 units, on average. For example, Overall satisfaction would differ by 0.60 when the attractiveness differs by 1 unit, keeping other attributes constant. Similarly, difference in Y (overall satisfaction) can be predicted for one unit difference in other Xn (marketing mix factors) individually, keeping the value of rest of the Xn constant.

In the regression model, each coefficient is influenced by the other variables in a regression model. Because independent variables are nearly always associated, two or more variables may explain the same variation in Y. So, each coefficient does not explain the total effect on dependent variable Y of its corresponding variable, as it would if it were the only variable in the model. Even though the model fit looks positive, the first section of the coefficients table shows that there are too many predictors in the model. There are several non-significant coefficients, indicating that these variables do not contribute much to the model.