CHAPTER 4: DATA ANALYSIS
4.3. Inferential Analyses
4.3.3. Multiple Regression Analysis
The multiple regression analysis will be conducted to analyse the significance of the online influential factors (personal eword-of-mouth, commercial eword-of-moth, liking towards website, and perceived informativeness of online advertisements) and the hotel brand image.
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Similarly to the analysis above, the data will be segmented into the three hotel categories for the online influential factors and the hotel brand image. Additionally, an analysis will be done to observe the significance between respondents who are checking-in, staying-in and have checked-out. This model can be referred to Chapter 3 under the respondent‟s hotel status.
4.3.3.1. Multiple Regression Analysis based on Hotel Categories
In this section, the analysis will be segmented into four categories. Firstly, based on the sample matrix, respondents from budget hotels, 3-star hotels, and luxury hotels will be calculated separately. The last category will include all respondents from the data collected.
Table 4.11. Model Summary for all Hotel Segmentation
Model Sample Size R R Square Adjusted R Square
Budget 150 .701a .492 .478
3-Star 150 .836a .698 .690
Luxury 154 .756a .571 .560
All 454 .746a .557 .553
a. Predictors: (constant), PEWOM, CEWOM, LTW, PIOA Source: Developed for research
Table 4.11 indicates that the R square for the multiple regression with all respondents was 0.557, or 55.7%. This would infer that 55.7% of the differences in the hotel brand image could be explained by the variance of the online influential factors.
A confidence internal of 95% was set for this research with the alpha value at 0.05. As a result, based on Table 4.12.1 – 4.12.4, the model in all four segments are significant as the value (0.000 for each segment) is below the alpha (0.05), with the F-value of 35.047, 83.926, 49.680, and 141.069 for budget, 3-star, luxury, and all respectively.
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Table 4.12. ANOVAb Table for All Hotel Categories
Model Sum of Sq df Mean Sq F Sig.
BUDGET N=150 Regression 24.747 4 6.187 35.047 .000a
Residual 25.597 145 .177
Total 50.344 149
3-STAR N=150 Regression 47.990 4 11.998 83.926 .000a
Residual 20.728 256 .143
Total 68.716 149
LUXURY N=154 Regression 39.504 4 9.876 49.680 .000a
Residual 29.620 149 .199
Total 69.123 153
ALL N=454 Regression 105.242 4 26.310 141.069 .000a
Residual 83.742 449 .187
Total 188.984 453
a. Predictors: (constant), PEWOM, CEWOM, LTW, PIOA b. Dependent variable: Hotel Brand Image
Source: Developed for research
Table 4.13.1. Summary of Regression Coefficienta for Budget Hotels (N = 150)
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(constant) 1.390 .287 4.850 .000
PEWOM .387 .048 .518 8.010 .000
CEWOM .212 .048 .281 4.376 .000
LTW .080 .053 .093 1.514 .132
PIOA .016 .040 .024 .401 .689
a. Dependent Variable: Hotel Brand Image Source: Developed for research
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Table 4.13.2. Summary of Regression Coefficienta for 3-Star Hotels (N = 150)
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(constant) .896 .231 3.883 .000
PEWOM .541 .038 .684 14.141 .000
CEWOM .239 .036 .318 6.632 .000
LTW .012 .045 .013 .273 .785
PIOA .013 .030 .021 .443 .658
a. Dependent Variable: Hotel Brand Image Source: Developed for research
Table 4.13.3. Summary of Regression Coefficienta for Luxury Hotels (N = 154)
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(constant) .693 .275 2.526 .013
PEWOM .289 .049 .360 5.884 .000
CEWOM .216 .047 .279 4.585 .000
LTW .300 .048 .356 6.247 .000
PIOA .098 .040 .138 2.487 .014
a. Dependent Variable: Hotel Brand Image Source: Developed for research
Table 4.13.4. Summary of Regression Coefficienta for All Hotel Categories (N = 454)
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(constant) .990 .156 6.348 .000
PEWOM .405 .027 .518 15.183 .000
CEWOM .215 .026 .282 8.335 .000
LTW .139 .028 .162 4.916 .000
PIOA .053 .021 .080 2.491 .013
a. Dependent Variable: Hotel Brand Image Source: Developed for research
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According to Table 4.13.4, all the online influential factors have p-value below the alpha of 0.05, which infers that all the variables have a positive correlation with the dependent variable (hotel brand image). However, when analysing further, by segmenting the data into three hotel categories, based on Table 4.13.1 and 4.13.2, the liking towards website and perceived informativeness of online advertisements have a p-value above 0.05. The budget hotel analysis has LTW and PIOA at 0.132 and 0.689 respectively; and the 3-star hotel analysis has LTW and PIOD at 0.785 and 0.658 respectively. This would indicate that these two online influential factors have a negative correlation with the dependent variable in the budget and 3-star segment.
4.3.3.2. Multiple Regression Analysis based on Respondent’s Hotel Status
This analysis will compare segmenting the respondents based on whether they have experience the hotel. The respondents who are checking-in, staying-in and have checked out will fall under Model A (Model A was also denoted as “All” in the analysis above); the respondents who are staying-in and have checked out will fall under Model B. The analysis comparing this two models are as follows:
Table 4.14. Model Summary based on Model A and Model B Segmentation Model Sample Size R R Square Adjusted R Square
A 454 .746a .557 .553
B 295 .728a .530 .523
a. Predictors: (constant), PEWOM, CEWOM, LTW, PIOA Source: Developed for research
According to Table 4.14., the R square for Model A and Model B are 0.557 and 0.530 respectively; this would infer that 55.7% and 53% of the differences of Model A and Model B in the hotel brand image could be explained by the variance of the online influential factors.
Table 4.15 indicated that the significant value for both models were 0.000 which is below the alpha of 0.05. The F-value for Model A was at 141.069 and for Model B at 81.638.
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Table 4.15. ANOVAb Table based on Model A and Model B
Model Sum of Sq df Mean Sq F Sig.
Model A N=454 Regression 105.242 4 26.310 141.069 .000a
Residual 83.742 449 .187
Total 188.984 453
Model B N=295 Regression 70.129 4 17.532 81.638 .000a
Residual 62.279 290 .215
Total 132.408 294
a. Predictors: (constant), PEWOM, CEWOM, LTW, PIOA b. Dependent variable: Hotel Brand Image
Source: Developed for research
Table 4.16.1. Summary of Regression Coefficienta for Model A (N = 454)
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(constant) .990 .156 6.348 .000
PEWOM .405 .027 .518 15.183 .000
CEWOM .215 .026 .282 8.335 .000
LTW .139 .028 .162 4.916 .000
PIOA .053 .021 .080 2.491 .013
a. Dependent Variable: Hotel Brand Image Source: Developed for research
Table 4.16.2. Summary of Regression Coefficienta for Model B (N = 295)
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
B Std. Error Beta
(constant) .815 .212 3.851 .000
PEWOM .393 .035 .479 10.873 .000
CEWOM .229 .035 .281 6.514 .000
LTW .150 .037 .171 .4024 .000
PIOA .086 .028 .125 .3055 .002
a. Dependent Variable: Hotel Brand Image Source: Developed for research
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Based on Table 4.16.1 and 4.16.2, both models indicated that all online influential factors are significant as all variables are below the alpha value of 0.05. Thus, we can conclude that based on our sample collected, it is assumed that regardless of respondent‟s status in the hotel, there is a significance of all online influential factors that influence the hotel brand image.