VFR TRAVELER

Factors influencing decision making and variety-seeking behaviour of visiting friends and relatives (VFR) traveller

Factors influencing decision making and variety-seeking behaviour of visiting friends and relatives (VFR) traveller

Analysis

Factor analysis for visiting friends and relatives traveller behaviour of Sri Lankan Expatriates to Sri Lanka is applied to know that the factors which are important and imperative for the Sri Lankan Expatriates to Sri Lanka.

KMO and Bartlett's Test

Kaiser Meyer Olkin Measure of Sampling Adequacy.

.784

Bartlett's Test of Sphericity

Approx. Chi-Square

1287.059

df

91

Sig.

.000

From the above table, it can be observed that the measure of Kaiser Meyer Olkin and Bartlett's test of sphericity assesses the sampling adequacy. The KMO statistic fluctuates between 0 and 1. If, the value of KMO for Sri Lankan Expatriates to Sri Lanka measures is 0 then it shows that the sum of partial correlations is largely comparative to the sum of the correlations, signifying dispersion in the pattern of correlations, for that reason, the factor analysis is expected to be inappropriate. However, in the given case, the value of KMO for Sri Lankan Expatriates to Sri Lanka measures is close to 1 which reflects that correlations patterns are comparatively compact and hence factor analysis for Sri Lankan Expatriates to Sri Lanka measures yield reliable and distinct factors. For this data the value is 0.784 which lies in the range of being superb that is close to 1; therefore, we should be confident that factor analysis is appropriate for this data.

In addition to this, the Bartlett test the null hypothesis that is the original correlation matrix for Sri Lankan Expatriates to Sri Lanka is an identity matrix. In this context, to apply the factor analysis, it is crucial and vital to have few relationships among the variables and if the rotated matrix is an identity matrix then all the correlation coefficients would be zero for the given study that is Sri Lankan Expatriates to Sri Lanka. In view of that, Bartlett test should be significant that is the significance value should below 0.05. In the above case, the significance value is less than 0.05 which shows significance of Bartlett test which presents that the R-matrix is not an identity matrix (Gorsuch, 2005). Consequently, it can be said that there is an existence of relationships among the incorporated variables; thus, the factor analysis is appropriate (Harman, 2006).

Total Variance Explained

Factor

Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

3.773

26.949

26.949

3.259

23.280

23.280

3.127

22.338

22.338

2

2.181

15.581

42.530

1.665

11.893

35.173

1.579

11.277

33.616

3

1.497

10.692

53.223

1.028

7.346

42.518

1.246

8.903

42.518

4

.958

6.841

60.064

5

.833

5.949

66.013

6

.807

5.761

71.774

7

.749

5.348

77.122

8

.635

4.539

81.660

9

.580

4.146

85.806

10

.487

3.482

89.288

11

.458

3.269

92.557

12

.413

2.952

95.510

13

.331

2.367

97.877

14

.297

2.123

100.000

Method of Extraction: Principal Axis Factoring.

The total variance explained shows the eigenvalues which are linked with each factor (linear component) before the extraction, after extraction and after rotation. It is found that before extraction, 14 linear components are identified. The eigenvalues related with each factor shows the variance explained by that distinct factor that is factor 1 which is explained as 23.2% of total variance. In addition to this, in the last part of the table, the eigenvalues of the factors after rotation are shown (Kline, 2004); thus, in view of rotation sums of squared loadings, it is observed that ...